Oceanography and Marine Biology, An Annual Review, Volume 40: An Annual Review: Volume 40 (Oceanography and Marine Biology)

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Author: R. N. Gibson | Margaret Barnes | R. J. A. Atkinson

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OCEANOGRAPHY and

M A R I N E B I OL OGY AN ANNUAL REVIEW

Volume 40

HH

OCEANOGRAPHY and M A RI N E B IO L O G Y A N A N N UA L RE V I E W Volume 40 Editors R. N. Gibson and Margaret Barnes The Dunstaffnage Marine Laboratory Oban, Argyll, Scotland e-mail: [email protected]

R. J. A. Atkinson University Marine Biological Station Millport, Isle of Cumbrae, Scotland e-mail: [email protected] Founded by Harold Barnes

First published 2002 by Taylor & Francis 11 New Fetter Lane, London EC4P 4EE Simultaneously published in the USA and Canada by Taylor & Francis Inc, 29 West 35th Street, New York, NY 10001 Taylor & Francis is an imprint of the Taylor & Francis Group This edition published in the Taylor & Francis e-Library, 2004. © 2002 R. N. Gibson, Margaret Barnes & R. J. A. Atkinson All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Every effort has been made to ensure that the advice and information in this book is true and accurate at the time of going to press. However, neither the publisher nor the authors can accept any legal responsibility or liability for any errors or omissions that may be made. In the case of drug administration, any medical procedure or the use of technical equipment mentioned within this book, you are strongly advised to consult the manufacturer’s guidelines. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging in Publication Data A catalog record for this book has been requested ISBN 0-203-18059-3 Master e-book ISBN

ISBN 0-203-23179-1 (Adobe eReader Format) ISBN 0-415-25462-0 (Print Edition)

CONTENTS

Preface

vii

Erratum to Vol. 39

viii

A review of sea-level research from tide gauges during the World Ocean Circulation Experiment

1

P. L. Woodworth, C. Le Provost, L. J. Rickards, G. T. Mitchum & M. Merriﬁeld

Coastal and shelf-sea modelling in the European context

37

J. E. Jones

Biogeochemistry of Antarctic sea ice

143

David N. Thomas & Gerhard S. Dieckmann

Accumulation and fate of phytodetritus on the sea ﬂoor

171

Stace E. Beaulieu

Impact of changes in ﬂow of freshwater on the Estuarine and open coastal habitats and the associated organisms

233

Bronwyn M. Gillanders & Michael J. Kingsford

A riot of species in an environmental calm: the paradox of the species-rich deep-sea ﬂoor

311

Paul V. R. Snelgrove & Craig R. Smith

Status and management of world sea urchin ﬁsheries

343

N. L. Andrew, Y. Agatsuma, E. Ballesteros, A. G. Bazhin, E. P. Creaser, D. K. A. Barnes, L. W. Botsford, A. Bradbury, A. Campbell, J. D. Dixon, S. Einarsson, P. K. Gerring, K. Hebert, M. Hunter, S. B. Hur, C. R. Johnson, M. A. Juinio-Meñez, P. Kalvass, R. J. Miller, C. A. Moreno, J. S. Palleiro, D. Rivas, S. M. L. Robinson, S. C. Schroeter, R. S. Steneck, R. L. Vadas, D. A. Woodby & Z. Xiaoqi

Temporal and spatial large-scale effects of eutrophication and oxygen deﬁciency on benthic fauna in Scandinavian waters – a review Karin Karlson, Rutger Rosenberg & Erik Bonsdorff

427

Mammals in intertidal and maritime ecosystems: interactions, impacts and implications

491

P. G. Moore v

CONTENTS

Author index

609

Systematic index

671

Subject index

680

HH

vi

PREFACE

The fortieth volume of this series contains nine reviews written by an international array of authors that, as usual, range widely in subject and taxonomic and geographic coverage. The majority of articles were solicited but the editors always welcome suggestions from potential authors for topics they consider could form the basis of appropriate contributions. Because an annual publication schedule necessarily places constraints on the timetable for submission, evaluation and acceptance of manuscripts, potential contributors are advised to make contact at an early stage of preparation so that the delay between submission and publication is minimised. The appearance of this volume is a milestone in two senses. First, it represents an afﬁrmation of the success of the series that has appeared annually for the last forty years and its continued appearance is a tribute to the vision of its founder, the late Harold Barnes. Secondly, it is the last volume with which Margaret Barnes will be associated as Editor. The series was ﬁrst published in 1963 and initially she was involved with the “Review” in an unofﬁcial capacity but following Harold’s untimely death in 1978, she ensured the uninterrupted continuation of the series by assuming the editorship herself. Since then a further 23 volumes have appeared under her guidance, latterly in collaboration with an expanded editorial team. The many authors with whom she has corresponded over the years will acknowledge her eye for consistency and detail as well as her courtesy in her dealings with them. The world of marine science is greatly in her debt for her longstanding contribution. The editors again gratefully acknowledge the willingness and speed with which authors complied with the editors’ suggestions, requests and questions and the efﬁciency of the copy editor and publishers in ensuring the regular annual appearance of each volume.

vii

ERRATUM TO VOL. 39

Kupriyanova, E. K., Nishi, E., ten Hove, H. A. & Rzhavsky, A. V. 2000. Life-history patterns in serpulimorph polychaetes: ecological and evolutionary perspectives. Oceanography and Marine Biology: an Annual Review, 39, 1–101. Figure 13 published in the above article was incorrectly printed and should be replaced by the one below. HH

viii

Oceanography and Marine Biology: anFRO Annual 2002, 40, SEA- LEVEL RESEARCH M Review TIDE G AUG E S1–35 © R. N. Gibson, Margaret Barnes and R. J. A. Atkinson, Editors Taylor & Francis

A REVIEW OF SEA-LEVEL RESEARCH FROM TIDE GAUGES DURING THE WORLD OCEAN CIRCULATION EXPERIMENT P. L. WOODWORTH 1 , C. LE PROVOST 2 , L. J. RICKARDS 3 , G. T. MITCHUM 4 & M. MERRIFIELD 5 1 Proudman Oceanographic Laboratory, Bidston Observatory, Birkenhead CH43 7RA, UK e-mail: [email protected] 2 Laboratoire d’Océanographie et de Géophysique Spatiale, GRGS/Observatoire Midi Pyrenées, 14 Avenue Edouard Belin, 31400 Toulouse, France 3 British Oceanographic Data Centre, Proudman Oceanographic Laboratory, Bidston Observatory, Birkenhead CH43 7RA, UK 4 Department of Marine Sciences, University of South Florida, 140 Seventh Avenue South, St Petersburg, Florida 33701, USA 5 University of Hawaii Sea Level Center, Department of Oceanography, University of Hawaii, 1000 Pope Road, Honolulu, Hawaii 96822, USA

Abstract This paper reviews the developments in tide gauge networks during the World Ocean Circulation Experiment (WOCE) and provides an overview of the resulting contributions to the scientiﬁc aims of the programme. The 1990s saw the rapid development of the satellite radar altimetry technique (results from which have been reviewed elsewhere), which played the major role in the measurement of ocean circulation variability during WOCE. This paper describes the complementary roles of altimetric and conventional in situ methods of sea-level recording by gauges which have evolved during the programme. In addition, it highlights those areas of research in which tide gauges (or bottom pressure recorders) have played a particularly important role. A ﬁnal section looks to the future “age of altimetry” wherein the sea level and ocean circulation community must strive to construct an efﬁcient, uniﬁed, global tide gaugeplus-altimetry system for application to a range of scientiﬁc objectives.

Introduction The World Ocean Circulation Experiment (WOCE) of the World Climate Research Programme (WCRP) has been the largest, international oceanographic experiment conducted to date. Its proposal during the mid-1980s was constructed to take into consideration the newly-developed capability for satellites to make near-global measurements of the ocean. In addition, agreements between almost 30 nations resulted in the most comprehensive set of deep-ocean hydrographic measurements so far, together with enhanced global capabilities for monitoring conventional in situ physical and chemical parameters. The ﬁrst phase of WOCE, its ﬁeld programme, lasted from 1990–97 and results from the ﬁrst phase have already provided a wealth of new insight into the ocean (Siedler et al. 2001). 1

P . L. WOODW ORTH, C. LE PROVOST , L . J. R I C K A R D S , E T A L .

One of the most innovative WOCE measurement types was undoubtedly sea level observed from satellite altimetry. The spectacular success of the satellite radar altimetry technique and, in particular, of the TOPEX/POSEIDON (T/P) mission, in providing precise, nearglobal and routine measurements of ocean topography assured the success of the WOCE ﬁeld programme itself. Altimetry is now an established technique of centimetric accuracy and complements perfectly the conventional in situ measurements of sea level by the global network of tide gauges. The role of altimetry in WOCE has recently been reviewed in an excellent paper by Fu (2001). The purpose of the present paper is to describe the measurements of sea level by gauges during WOCE, their particular scientiﬁc importance and their complementary value to sea-level measurements by altimetry. Many of the papers referred to will have a publication date after 1997. This is a reﬂection of the fact that WOCE is continuing in its second phase (called AIMS, see p. 27) and that the datasets collected during the ﬁeld programme will continue to provide an invaluable resource to ocean circulation studies for many years. The paper is organised as follows. First, the background to the requirement for sea-level data during the WOCE programme is outlined and this background is followed by a description of the evolution and present status of the WOCE sea-level network. The next two sections review the developing conﬁdence in the agreement between sea-level data from tide gauges and altimetry and from model sea-level information that took place during the 1990s. They also describe the resulting construction of methods for ongoing calibration of altimetry using (mostly island) tide gauges. The particular WOCE interest in the study of ﬂows through the “choke points” of the Antarctic Circumpolar Current (ACC) by means of bottom pressure and coastal tide gauge data is then outlined. Other papers concerned with ocean variability on typically seasonal and longer timescales that have been published during WOCE and which are based, at least in part, on tide gauge information are then reviewed. The subsequent section reviews developments in deep ocean tide models, most of which were enabled by advances in altimetry, but which have required tide gauge data for validation. In addition, this section points to the extension of global tide model accuracy into shallow water areas through assimilation of coastal gauge data. The penultimate section describes the dramatic advances in new geodetic techniques for application to a range of ocean and sea-level studies. Finally, the future role of the global tide gauge network for ocean circulation and climate studies is discussed.

The WOCE programme and its sea-level requirements The ﬁeld phase of WOCE had two goals as agreed at the WOCE International Conference in 1988 (WOCE 1989). The ﬁrst goal was to develop models useful for predicting climate change and to collect the data necessary to test them, with an expectation that by the end of the ﬁeld programme computers would be capable of running the global eddy-resolving models required for climate studies. The second goal was to determine the representativeness of the speciﬁc WOCE datasets for describing the long-term behaviour of the ocean, and to ﬁnd methods for determining long-term changes in the ocean circulation. WOCE planning included a strategy for achieving both goals in terms of three core projects which were elaborated upon in the WOCE Implementation Plan (WOCE 1988a,b). 2

SEA- LEVEL RESEARCH FRO M T I D E G A U G E S

The three core projects (CPs) were: CP-1 The Global Description; CP-2 The Southern Ocean; CP-3 The Gyre-Dynamics Experiment. The ﬁrst two core projects provided the main justiﬁcations for sea-level measurements during WOCE. The necessity for providing a global description of ocean circulation variability in CP-1 resulted in the Implementation Plan’s call for a quasi-global sea-level network using altimetry and gauges in combination. Sea-level measurements at island tide gauges were to be used, in effect, as satellite tracking data, to provide long wavelength corrections to the relatively poorly determined satellite orbits of the time. Consequently, considerable discussion took place on how many gauges would be “enough”. While it was generally agreed that gauges would also be required at special locations for ocean circulation measurement such as “choke points”, there seemed at the end of the 1980s to be an emphasis in the WOCE community (but not necessarily in the WOCE sea-level community) that a few 10s of island gauges would be sufﬁcient for scientiﬁc requirements, given the availability of altimetry. However, aside from their use in one or two feasibility studies of large-scale sea surface variability and ocean circulation (e.g. Wunsch 1991a,b), island tide gauge data were never employed as tracking data as originally intended. The radical improvement in parameterisation of the Earth’s gravity ﬁeld, uncertainties in which were the main source of errors in satellite orbit computations, resulted in the disappearance of “tilt and bias” and other ad hoc schemes (such as the use of island gauges) to reduce orbit error in altimeter data (Tapley et al. 1994). An altimeter satellite such as T/P became essentially a “tide gauge in space”, with a sea surface height accuracy of approximately 4 cm (Fu et al. 1996). Tide gauge data became employed primarily as a source of validation information for time series of altimeter seasurface heights, as a special source of sea-level data at ocean boundaries and straits where the spatial-temporal sampling of altimetry is not optimal. Most recently, as the T/P time series has extended beyond 8 yr, they have been used as a source of long-term calibration information to the overall altimetric measurement system. CP-2 had special importance for WOCE given the Southern Ocean’s inﬂuence on the water masses of the entire world ocean through the inter-ocean linkage of the ACC with a transport of approximately 130 Sv (1 Sv = 106 m3 s−1), and given its importance to deep water formation and other ventilation processes that affect global climate. A recent special issue of the Journal of Geophysical Research (Vol. 106, C2) contains a number of papers discussing aspects of Southern Ocean and ACC science studied during WOCE. An updated review of the ACC system is given by Rintoul et al. (2001). CP-2 demonstrated the advantages of conventional coastal or deep sea gauges (of which the latter are usually referred to as bottom pressure recorders, BPRs) in particular situations. The ﬁrst was with regard to measurements at the “choke points” of the ACC which include the Drake Passage, southern Indian Ocean (e.g. Amsterdam–Kerguelen) and Australia– Antarctica. In the case of choke points or straits, pairs of coastal gauges or BPRs are a better choice for circulation monitoring than altimetry, with its long “return periods” of several days to weeks. A second advantage comes from the need for measurements in ocean areas which might either be ice-covered for a large part of the year or which, in the case of T/P, might be at latitudes higher than the inclination of the satellite. The operation of gauges in “environmentally hostile regions” became the subject of international working groups (IOC 1988, 1991, 1992) with the result that the Antarctic component of the global tide gauge network was enhanced signiﬁcantly. Meanwhile, technical developments in the use of BPRs for WOCE resulted in the provision of instruments that could be deployed safely in deep waters for up to 5 yr (Spencer & Vassie 1997). 3

P . L. WOODW ORTH, C. LE PROVOST , L . J. R I C K A R D S , E T A L .

Sea-level measurements for WOCE Altimeter and tide gauge plans for WOCE The potential for global measurements of sea-level changes by means of satellite radar altimetry had been demonstrated prior to the start of WOCE by the Skylab (launch 1973), GEOS-3 (1975), Seasat (1978) and Geosat (1985) missions. All of these projects were funded by US agencies with data made available freely to the international community (although with a delay of several years in the case of the Geosat Geodetic Mission). This spirit of international co-operation within a national (or bi-lateral) programme reached new heights with the US/French T/P (1992) project, the planning for which beneﬁted considerably from the establishment of an international Science Working Team (SWT) several years prior to launch. The relatively high-altitude T/P altimeter satellite was the ﬁrst to be designed speciﬁcally for the study of large-scale ocean circulation and ocean tides. Collaboration also played a part in planning for the much-delayed European ERS-1 (1991) mission, although not to the same extent as for T/P. A consequence of these activities was that by the effective start of WOCE in the early 1990s, an international community of oceanographic researchers was in place ready to exploit the upcoming altimetric sea-level datasets, with which researchers were provided essentially free of charge. T/P and ERS-1, replaced eventually by ERS-2 (1995), performed excellently through the 1990s and continue to provide high-quality data to the present day (Fu 2001, Fu & Cazenave 2001). The Geosat Follow-On (1998) has not so far produced the anticipated datasets. At the time of writing, we expect the US/French JASON-1 satellite to be launched in December 2001 and to eventually replace T/P, while the European Envisat mission will be launched in March 2002 to continue the ERS time series. Further missions are anticipated for launch throughout the next decade. To many researchers, the apparent ease with which the delivery of altimeter data could be made to researchers by space agencies contrasted with the many difﬁculties experienced by the international group of tide gauge operators in supplying complementary in situ information. Why, one was sometimes asked, cannot a suitable near-global set of tide gauge data be collected without major effort and with a fraction of the cost of the satellites? Surely such data must be routinely available? This criticism (however understandable) stemmed from a lack of knowledge of the way in which tide gauge data were acquired on a global basis, and of the fact that the “global network” was constructed from a set of disparate national gauge sites, data from which were pooled by voluntary national contributions to international databanks. Given that funds (however small) for the in situ component were not forthcoming from the space agencies or the WOCE programme itself, the tide gauge network for WOCE had to be constructed around that which had been assembled for earlier, regional programmes. This included, in particular, the network developed by the University of Hawaii for the Tropical Ocean Global Atmosphere (TOGA) programme (Kilonsky & Caldwell 1991, McPhaden et al. 1998) and the Integrated Global Ocean Services System (IGOSS) sea level project (Kilonsky et al. 1997). Other elements included gauges at islands in the South Atlantic operated by the Proudman Oceanographic Laboratory (POL) (Spencer et al. 1993), and in the southern Indian Ocean by French groups (Le Provost et al. 1995b). The responsibility

4

SEA- LEVEL RESEARCH FRO M T I D E G A U G E S

for data ﬂow from these sites to researchers was meanwhile delegated to two centres at the University of Hawaii Sea Level Center (UHSLC) and the British Oceanographic Data Centre (BODC) at POL, a task-sharing arrangement which has functioned well for most of the programme (Kilonsky et al. 1999). Sea-level data collection from tide gauges remains essentially multinational in character. There is still no one large source of funding with which to operate a coherent global in situ network. Nevertheless, experience during WOCE, and within the wider context of the Intergovernmental Oceanographic Commission (IOC) Global Sea Level Observing System (GLOSS) programme (p. 14), has been valuable in convincing individual national tide gauge agencies of the international importance of their data and thereby of the necessity to maintain and upgrade tide gauge installations.

The WOCE Tide Gauge Network The WOCE Tide Gauge Network has evolved during the 1990s into that described by Figure 1 and Tables 1, 2 and 3. The network currently comprises 160 stations reporting in “delayed mode” and 127 in “fast mode”. With a few exceptions, all of the fast mode data are incorporated into the delayed mode dataset. However, the delayed mode dataset includes additional data that are not available in real or near-real time. The data are usually supplied as hourly values, but in some cases may be more frequent (e.g. 6 min and 15 min). During the WOCE period, efforts have been made by several countries to install new tide gauges, especially in the polar regions. For example, Denmark has installed several gauges around the coast of Greenland, and Australia, France and the UK have installed gauges on islands in the Southern Ocean and around the coast of Antarctica. These installations have improved the distribution of gauges and enhanced the global sea-level dataset. In parallel, the number of gauges delivering their data in near-real time has increased. The WOCE Data Handbook (WOCE, 1994) stated that hourly, or more frequent, observations of sea level are required and, for locations at mid or high latitudes, the tide gauge measurements should be supplemented, wherever possible, by sea-level atmospheric pressure data. The strategy for tide gauges in WOCE was to take advantage of the existing extensive regional networks such as those provided by the GLOSS and TOGA programmes and to extend them in accordance with the following needs: (1) (2) (3)

to complement altimetric measurements in oceans with sparse island distributions; to instrument the high latitude Southern Ocean, both to complement altimetric measurements and as an independent measure of variability in a poorly observed region; to instrument straits and channels which can be monitored by surface elevation measurements and through which there is considerable transport (e.g. Drake Passage).

Under the WOCE data management system, data ﬂows from the data collectors to Data Assembly Centres (DACs) and Special Analysis Centres (SACs). The data are quality assured and then distributed to end-users and archived. The DACs and SACs ensure the direct involvement of research groups in the management of WOCE datasets. When WOCE

5

Figure 1

The WOCE Tide Gauge Network. Fast and Delayed Mode stations are indicated by circles and triangles, respectively.

P . L. WOODW ORTH, C. LE PROVOST , L . J. R I C K A R D S , E T A L .

6

SEA- LEVEL RESEARCH FRO M T I D E G A U G E S

Table 1 Station Name

Ammassalik, Greenland Ascension, United Kingdom Atlantic City, NJ, USA Basques, Canada Bermuda, St Georges Is., UK Cape Hatteras, North Carolina, USA Ceuta, Spanish N Africa Charleston, South Carolina, USA Churchill, Canada Dakar, Senegal Diego Ramirez, Chile Duck, North Carolina, USA Edinburgh, Tristan da Cunha Esperanza, Argentina (Antartica) Exuma, USA Faraday, Argentine Is. Fortaleza, Brazil Fort Pulaski, Georgia, USA Galveston, Texas, USA Gibraltar

WOCE Atlantic Ocean sea-level stations.

GLOSS No. 228 263 220 221

UHSLC No.

Latitude (N +ve)

Longitude (E +ve)

Fast-mode data

Delayed-mode data 1990–1996 1983–1998

291

65.50 −7.92

−37.00 −14.42

1993–2000

264 273 259

39.35 47.57 32.37

−74.42 −59.13 −64.70

1985–2001 1997–2001 1985–1999

35.14

−75.03

9006 249 9039

261

35.90 32.78

−5.32 −79.93

1985–2001

9056 253

274 223

58.78 14.67

−94.02 −17.04

1985–2001 1994–2001

180 219 266 185

599 260

−56.52 36.18 −37.50 −63.40

−68.72 −75.07 −12.03 −56.98

1993–1998 1985–2001

23.77 −65.25

−76.01 −64.27

283 752

−3.43 32.03

−38.47 −80.90

2000 1985–2001

217 248

775

29.31 36.12

−94.79 −5.04

1985–2001

222 265

275

44.67 −20.50

−63.58 −29.03

1985–2001

601

12 188

289

1961–1997 1982–1989, 1992–1995 1991 1979–1997 1984–1995

1992–1993 1959–1971, 1984–1997

9019 216

242

69.22 24.55

−51.01 −81.81

1985–2001

Lerwick, United Kingdom

236

293

60.15

−1.13

1993–2001

Little Cornwallis, Canada Lome, Togo Miami, Haulover Pier, USA Newport, Rhode Is., USA Newlyn, United Kingdom Nuuk/Godthaab, Greenland Palmeira, Cape Verde

153 224

75.23 6.13 25.90

−96.57 1.28 −80.12

1989–1993

1961–1990, 1993–1996 1920–1997 1974–1975, 1993 1992–1996 1926–1954, 1969–1995, 1997 1959–1978, 1980–1999 1986–1994 1982–1992

290

253

41.51

−71.33

1985–2001

241 225

294

50.10 64.17

−5.06 −51.73

1985–2001

235

16.75

−22.98

2000

7

1971–1991 1985–1996

1996–1998

Halifax, Canada Ilha da Trindade, Trinidad and Tobago Ilulissat, Greenland Key West, Florida, USA

218

1968–1989, 1991–1995 1992–1997

1915–1999 1985–1995

P . L. WOODW ORTH, C. LE PROVOST , L . J. R I C K A R D S , E T A L .

Station Name

Penedo Sao Pedro e Paulo, Brazil Pensacola, Florida, USA Ponta Delgada, Azores

Port Stanley, United Kingdom

GLOSS No.

Table 1

continued

UHSLC No.

Latitude (N +ve)

199

Longitude (E +ve)

0.92

−29.34

Fast-mode data

1982–1985

288 245

762 211

30.44 37.74

−87.21 −25.67

1985–2001 1994–2001

305

290

−51.75

−57.93

1992–2000

726 245 225

16.87 −54.93 60.72 64.15 −3.85 18.46 0.17

−24.98 −67.62 −46.33 −21.93 −33.82 −66.12 6.51

1999–2000 1985–2001 1988–1996

Porto Grande, Cape Verde Puerto Williams, Chile Qaqortoq, Greenland Reykjavik, Iceland Rocas, Atol Das, Brazil San Juan II, Puerto Rico Sao Tome, Sao Tome

254 9003 9020 229

Settlement Point, USA Siboney, Cuba Signy, South Orkney Is. Sisimiut, Greenland South Caicos, UK St. Croix, Virgin Is.

211 215 306 9021 296 9011

257

26.77 23.92 −60.70 66.93 22.00 17.70

−79.00 −82.47 −45.06 −53.67 −72.00 −64.77

1985–2001

264 223 238

292 276 295

−15.97 47.57 58.22

−5.07 −52.72 −6.38

1993–2001 1993–2001 1985–2001

237 181 9023

600

62.00 −54.80 25.73

−6.77 −68.03 −80.16

1996–2001

St. Helena, UK St. John’s, Canada Stornoway, UK Torshavn, Faroe Islands Ushuaia, Argentina Virginia Key, Biscayne Bay

206 260

Delayed-mode data

1978–1980, 1982–1991, 1993–1995, 1998 1964–1969, 1974, 1988–1989, 1991–1998 1990, 1993 1964–1998 1991–1996 1984–1997 1985–1998 1985–1986, 1988 1985–1993 1990 1988–1996 1991–1996 1991–1992 1991–1993, 1996–1997 1986–1998 1961–1996 1974–1983, 1985–1999 1985–1995 1996–1998 1994–1997

recognised the need for in situ sea-level data, it was only natural to take advantage of the experience that already existed at both UHSLC and BODC, and request that they become WOCE DACs. The UHSLC was established as the “fast mode” DAC and tasked with the assembly, quality control and distribution of all sea-level data from WOCE gauges delivered by satellite or other near-real time systems. The data were to be made available to investigators in a time frame of 1–3 months after data collection. The BODC, as the “delayed mode” DAC, was to assemble and supply sea-level data from the WOCE network to the full extent of quality control. Distribution was to be within 18–24 months after data collection. BODC was also tasked to ensure archival of the sea-level data as a WOCE dataset in the World Data Centre (WDC) system by the end of the WOCE experiment. 8

SEA- LEVEL RESEARCH FRO M T I D E G A U G E S

Table 2 Station Name

WOCE Indian Ocean sea level stations.

GLOSS No.

UHSLC No.

Latitude (N +ve)

Longitude (E +ve)

−10.42

105.67

Fast-mode data

Delayed-mode data 1986–1987, 1990–1991 1985–2000

Christmas Is., Australia

47

Cocos Is., (Keeling), Australia Crozet Island, France Darwin, Australia Diego Garcia, UK

46

171

−12.12

96.09

1985–2001

21 62 26

178 168 104

−46.43 −12.47 −7.29

51.87 130.09 72.39

1995–2001 1985–2001 1988–2000

13 54 27 9041

176 109 117

−29.87 −33.87 −0.69 6.77

31.52 121.09 73.15 73.17

1985–2001 1987–2001 1991–2001

− 49.35 4.23 4.18

70.02 100.62 73.53

Durban, South Africa Esperance, Australia Gan, Republic of Maldives Hanimaadhoo, Republic of Maldives Kerguelen Island, France Lumut, Malaysia Male (Hulhule), Republic of Maldives Mawson, Antarctica Mombasa, Kenya Point La Rue, Seychelles Port Elizabeth, South Africa Port Louis Harbour, Mauritius Port Victoria, Hodoul Is., Seychelles Rodrigues, Mauritius Salalah, Oman Simonstown, South Africa St. Paul Island, France Zanzibar, Tanzania

23 43 28

180 108

1993–2001 1989–2001

22 8 9042 76

101 121

−67.60 −4.70 − 4.67 −33.96

62.88 39.66 55.53 25.06

1986–2000 1993–2001

18

103

−20.16

57.05

1986–2001

−4.62

55.46

−19.67 16.94 −34.19 −38.72 −6.16

63.42 54.07 18.04 77.58 39.02

273 19 4 268 24 297

105 114 179 151

1986–2001 1989–2001 1994 –2001 1985–2001

1993–1997 1984–1998 1969, 1988–1998 1970–1997 1990–1998 1987–1998 1991–1998 1993–1998 1984–1995 1988–1998 1991–1997 1986–1998 1993–1998 1973, 1978–1997 1942–1947, 1964–1965, 1986–1998 1977–1982, 1986–1992 1986–1997 1989–1998 1958–1996 1994–1998 1984–1998

The “fast mode” sea level DAC The creation of the WOCE DAC necessitated a major new initiative for the UHSLC. Before it was established, sea-level data were collected, processed, and distributed within 1–2 yr after the calendar year of the data collection. The WOCE “fast mode” DAC, on the other hand, was to provide information needed to check the altimeter data against the more traditional and well-understood sea-level data from the tide gauges. The altimetry data were available within a month or so of collection. Thus, the UHSLC had to process data from a globally distributed set of stations and make the in situ sea-level data available to users on a comparable timescale. The turn around time for this dataset is much faster than for the TOGA dataset, and the geographical extent of the dataset exceeds that of the earlier TOGA and IGOSS datasets. Fortunately, the UHSLC had access to a large fraction of the open ocean 9

P . L. WOODW ORTH, C. LE PROVOST , L . J. R I C K A R D S , E T A L .

Table 3

WOCE Paciﬁc Ocean sea-level stations.

Station Name

GLOSS No.

UHSLC No.

Latitude (N +ve)

Longitude (E +ve)

Fast-mode data

Delayed-mode data

Aburatsu, Japan Adak Island, Alaska, USA Apra Harbor, Guam Arica, Chile Balboa, Panama Baltra, Galapagos Is.

82 302 149 9001 168 169

354 40 53 83 302 3

31.57 51.86 13.43 −18.47 0.96 −0.44

131.42 −176.06 144.07 −70.33 −79.57 −90.03

1985–2001 1985–2001 1985–2001 1985–1999 1985–2001 1985–2001

1961–1998 1950–1997 1948–1997 1982–1998

113

2

1.37

172.93

1988–2001

79

−46.60 −24.83 22.88 −27.67 −12.50 −67.57 −43.95

168.04 152.04 −109.91 −70.83 −77.02 −68.13 −176.57

9058 103 146

47 11

23.88 27.10 1.98

121.38 142.18 −157.47

1985–2001 1985–2001

9043

556

41.75

−124.18

1985–2001

57 158

41 333 551

7.83 53.90 −33.85 37.81

125.63 −166.05 151.23 −122.05

1992–2001 1985–2001 1985–2001

1985–1998 1901–1996

107

14

23.87

−166.03

1985–2001

1974–1998

121 88

25 364

−8.53 41.78

179.22 140.73

1985–2001 1985–2001

287

60

19.73

−155.67

1985–2001

−42.88

147.33 159.96 −157.87 121.58 124.02 168.62 −169.52 −78.83

1977–1998 1967, 1969–1998 1927–1932, 1946–1997 1985, 1987–1995 1974–1998 1905–1997 1984–1993 1975–1998

1999–2001 1985–2001 1990–1998

−156.47 −171.72

1985–2001

Betio, Tarawa, Gilbert Is., Kiribati Bluff Harbour, New Zealand Bundaberg, Australia Cabo San Lucas, Mexico Caldera, Chile Callao, Peru Cendering, Malaysia Chatham Island, New Zealand Chen Kung, Taiwan, China Chichijima, Japan Christmas, Line Is., Kiribati

Crescent City, California, USA Davao, Philippines Dutch Harbor, Alaska, USA Fort Denison, Australia Fort Point (San Francisco), California, USA French Frigate Shoals, Hawaii, USA Funafuti, Ellice Is., Tuvalu Hakodate, Japan Hilo, Hawaii, USA

129 59 161 9002 173 293 128

332 34 88 93

71

Hobart, Australia

9018

Honiara, Solomon Islands Honolulu, Hawaii, USA Hualien, Taiwan, China Ishigaki, Japan Jackson Bay, New Zealand Johnston Island, USA Trust Juan Fernandez, Chile

66 108 9014 9015 109 176

403 52 21

−9.43 21.31 23.97 24.33 −43.98 16.75 −33.62

Kahului, Hawaii, USA Kanton, Phoenix Is., Kiribati

9025 145

13

20.90 −2.81

9 57

10

1985–2001 1985–2001 1985–2000 1985–2001

1968–1977, 1985–1998 1974–1998 1984–1989 1997–1998 1973–1998 1980–1998 1985–2000 1990–1995

2000–2001

1985–2001 1985–2001

1993–1994 1975–1997 1955–1963, 1965–1972, 1974–1998 1951–1984, 1996–1997 1984–1990

1947–1997 1977–1979, 1981–1988, 1990–1998 1950–1997 1949–1967, 1972–1998

SEA- LEVEL RESEARCH FRO M T I D E G A U G E S

Station Name Kapingamaringi, Caroline Is, Fd. St. Micronesia Ketchikan, Alaska, USA Kodiak Island, Alaska, USA Kushimoto, Japan Kushiro, Japan

GLOSS No.

Table 3

continued

UHSLC No.

Latitude (N +ve)

Longitude (E +ve)

Fast-mode data

Delayed-mode data

117

29

1.98

154.78

1985–2001

1978–1998

9046 9047 85 89

571 39 353 350

55.33 57.73 33.47 42.97

−131.63 −152.52 135.78 144.38

1985–2001 1985–2001 1985–2001 1985–2001

Kwajalein, Marshall Island

111

55

8.73

167.73

1985–2001

La Jolla (San Diego), California, USA La Libertad, Ecuador Lautoko, Fiji Lobos De Afuera, Peru Lombrum, Papua New Guinea Macquarie Is., Australia Majuro, Marshall Islands

159

569

32.72

−117.17

1985–2001

1949–1997 1975–1997 1961–1997 1963, 1965–1998 1946–1995, 1997 1906–1997

172 9048 9027

91 402 84 400

−2.20 −17.60 −6.93 −2.33

−80.92 177.43 −80.72 147.37

1985–2001 1992–2001 1985–1999 1994–2001

1982–1994 1994–1998

130 112

5

−54.48 7.10

158.97 171.37

1985–2001

134.46 125.19 −104.33

1985–2001

Malakal, Belau Manado (Bitung), Indonesia Manzanillo, Mexico

Mera, Japan

120 69 163

7 395

7.33 1.44 19.50

86

352

34.92

139.83

1985–2001

1992–2001

Midway Island, Hawaii, USA Moturiki, New Zealand Nagasaki, Japan

106

50

28.22

−177.37

1985–2001

9022 83

362

−37.65 32.73

176.18 129.87

1985–2001

Naha, Japan Naos Island, Panama Nauru, Gilbert Is., Kiribati Nawiliwili, Hawaii, USA Naze, Japan

81 9049 114 9024 9016

355 300 4

26.22 8.92 −0.53 21.97 28.38

127.67 −79.53 166.09 −159.04 129.05

1985–2001 1991–1998 1985–2001

Neah Bay, Washington, USA Nishinoomote, Japan Noumea, New Caledonia Nuku Hiva, Marquesas Is.

9068

558

48.37

−124.62

1985–2001

9017 123 142

19 31

30.73 −22.29 −8.93

130.10 166.44 −140.82

1985–2001 1985–1998

Nuku’alofa, Tonga Ofunato, Japan Pago Pago, American Samoa Papeete, Tahiti Pascua (Easter) Island, Chile

9051 87 144 140 137

38 351 56 15 22

−21.13 39.67 −14.28 −17.53 −27.15

−175.17 141.72 −170.68 −149.57 −109.05

1990–2001 1985–2001 1985–2001 1985–2001 1985–2001

11

1993–1997 1968–1972, 1974–1998 1969–1998 1986–1990 1953–1959, 1961–1982, 1992–1998 1965, 1967–1997 1947–1997 1995 1964, 1968–1998 1966–1998 1991–1997 1974–1998 1954–1997 1965–1974, 1976–1998 1934–1997 1965–1998 1967–1998 1982, 1986–1997 1990–1998 1965–1998 1948–1997 1969–1999 1957–1958, 1962–1963, 1977–1998

P . L. WOODW ORTH, C. LE PROVOST , L . J. R I C K A R D S , E T A L .

Station Name

GLOSS No.

Table 3

continued

UHSLC No.

Latitude (N +ve)

Longitude (E +ve)

Fast-mode data

Delayed-mode data

143

24

−8.98

−158.53

1985 –2001

1977–1998

115

1

6.98

158.03

1985–2001

9026

46

−17.77

168.03

1993–2001

Prince Rupert, Canada

155

540

54.32

−130.33

1985–2001

Provideniya, Russia Prudhoe Bay, Alaska, USA

309 151

579

64.40 70.20

−173.02 −148.03

1994–2001

Quepos, Costa Rica

167

87

9.40

−84.17

1985–1995

65

10

−4.20

152.02

1985–2001

139 138 118 177

23 16 28 35

−21.21 −23.13 15.23 −26.28

−159.08 −134.95 145.74 −80.13

1985–2001 1985–2000 1985–2001 1992–1997

9053 150 154 162 157 56 9057 122 9054 156 60 116

30 560 559 90 592 335

−0.75 60.17 57.05 18.73 44.63 −42.55 24.59 −18.13 −4.58 49.15 −19.25 7.45

−90.31 −149.43 −135.34 −111.17 −124.04 147.93 121.87 178.43 −81.28 −125.92 146.83 151.09

1985–2001 1996–2001 1994–2001 1992–1997 1996–2001 1985–2001

1969–1971, 1974–1998 1977–1982, 1993–1998 1909–1922, 1963–1995 1977–1989 1992–1993, 1995–1997 1961–1965, 1971–1994 1966–1971, 1974–1997 1977–1998 1969–1998 1978–1998 1987–1993, 1996–1997 1978–1998

Penrhyn, Cook Islands Pohnpei, Caroline Is., Fd St. Micronesia Port Vila, Vanuatu

Rabaul, Papau New Guinea Rarotonga, Cook Islands Rikitea, Gambier Saipan, Mariana Islands San Felix, Chile Santa Cruz, Galapagos Is. Seward, Alaska, USA Sitka, Alaska, USA Socorro Is., Mexico South Beach, Oregon, USA Spring Bay, Australia Suao, Taiwan, China Suva, Fiji Talara, Peru Toﬁno Townsville, Australia Truk Atoll, Caroline Is., Fd St. Micronesia Valparaiso, Chile

Wake Island, Marshal Is. Yakutat Bay, Alaska, USA Yap, Caroline Is, Fd. St. Micronesia

18 92 334

1985–2001 1992–1996 1985–2001

175

81

−33.33

−71.63

1985–2000

105

51

19.28

166.62

1985–2001

9055 119

570 8

59.55 9.51

−139.73 138.13

1985–2001 1985–2001

1991–1998 1981–1992 1972–1998 1950–1965 1963–1996 1985–1998 1963–1995 1944–1974, 1977–1978, 1982–1998 1950–1967, 1969–1997 1961–1997 1951–1952, 1969–1970, 1973–1998

gauges that were capable of reporting sea-level data quickly enough to meet the near-real time requirement. The UHSLC also had experience with distributing data and data products on the required timescale via the IGOSS sea level project. The UHSLC in situ sea-level stations employ a robust design that emphasises redundancy of measurements including an automated switch that produces reference level information (Mitchum et al. 1994). These stations provide long-term sea-level monitoring accurately 12

SEA- LEVEL RESEARCH FRO M T I D E G A U G E S

related to a datum at the millimetre level using inexpensive ﬂoat-operated as well as acoustic gauges. They upload data to the UHSLC via the National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite (GOES) Data Collection System (DCS), Japan’s Geostationary Meteorological Satellite (GMS) DCS, and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteorological Satellite (METEOSAT) DCS, and provide the nucleus of the WOCE “fast mode” dataset. The UHSLC has also expanded upon the contacts made with other national agencies contributing sea-level data to the TOGA and IGOSS projects. Through these activities, described more fully by Kilonsky et al. (1997), the UHSLC has signiﬁcantly augmented their collection of near-real time sea-level data.

The “delayed mode” sea-level DAC BODC’s role as the “delayed mode” DAC is to assemble, distribute and supply sea-level data to the full extent of quality control possible covering all of the gauges in the network. WOCE requirements are that the elevations should be accurate to 1 cm, the timing to 2 min and the atmospheric pressure measurements to 1 mbar. Operation of the “delayed mode” sea level DAC meant an expansion of BODC’s previous sea-level activities to complement the “fast mode” centre activities described above. Data collected by the UHSLC as part of the TOGA dataset provided a signiﬁcant element of the initial dataset but contacts were also initiated with some 25 organisations around the globe requesting their sea-level data for WOCE. Data are requested annually, with atmospheric pressure data requested in addition to sea level, although these have not proved easy to obtain. Special effort has gone into obtaining data from gauges which not readily accessible in near-real time. In addition to the sea-level data collected from standard tide gauges, BODC also has the responsibility for data collected by bottom pressure recorders and inverted echo sounders. Most of the contribution for this part of the dataset comes from the POL’s ACCLAIM (Antarctic Circumpolar Current Levels by Altimetry and Island Measurements) network in the South Atlantic.

WOCE tide gauge datasets and products The major product from the two WOCE Sea Level DACs is a quality controlled tide gauge dataset (Rickards & Dowell 1992, Dowell & Rickards 1993). Both organisations carry out their own quality control, and although there are some differences in details, the essential elements are the same. These centre on tidal analysis of the data, and comparisons of data between neighbouring sites, to resolve any problems of timing drifts and to maintain reference level stability. Obvious spikes in the data are removed and gaps are documented. Any remaining questionable ﬂuctuations are carefully checked to determine whether the ﬂuctuation is a real event or perhaps an indication of a mechanical problem with the tide gauge. Documentation is assembled describing the tide gauge and its site, benchmarks, levelling and datum history, peculiar characteristics of the tide gauge (e.g. complex local bathymetry, seiching, silting up of the harbour, river mouths) and summarising the data completeness and quality. Both DACs place a strong emphasis on discussion of the data with the supplier to resolve any problems and to improve data quality. 13

P . L. WOODW ORTH, C. LE PROVOST , L . J. R I C K A R D S , E T A L .

The UHSLC is presently collecting data from 127 stations for the “fast mode” component. These include contributions from the Laboratoire d’Océanographie et de Géophysique Spatiale (LEGOS) in France (5 stations), the Australian National Tidal Facility (ANTF) (19 stations), Canada (5 stations), Brazil (2 stations), BODC in the UK (6 stations), the US National Ocean Service (31 stations), and, building upon their contribution to the IGOSS maps, the Japanese Meteorological Agency (8 stations along the coast of Japan). For 82 “fast mode” stations, the existing time series have been extended backward to 1985 in order to connect Geosat and T/P datasets. The “fast mode” holdings now include over 1650 site-yr of hourly data. The BODC is at present collating data from approximately 160 tide gauge sites worldwide for the WOCE “delayed mode” DAC. Data from these have been supplied from 20 countries. All historical data from these tide gauges have been requested in addition to the data collected during the WOCE period. Several sites have data extending back over 80 yr and approximately 35 extend over 40 yr. The total volume of data received so far is over 3550 site-yr. Most data are supplied in digital form, but occasionally historical manuscript data has been received and digitised, for example from Gibraltar. Included in the “delayed mode” dataset are data from the ACCLAIM network in the South Atlantic (seven island and Antarctic tide gauges plus bottom pressure gauge data). These latter are from deployments at about 20 locations. At present 50 records, each usually of approximately 1 yr in length, have been collected. Data from both WOCE DACs are in high demand. Regular requests for sea-level data from WOCE scientists are received. However, since the establishment of the public directories (i.e. ftp sites), and web sites at the DACs, most requests are serviced by the scientists themselves, and a record of them logged. Since the inception of WOCE, advances in web and internet technologies have enabled the UHSLC DAC to present an increasing volume of near-real time data online, and to participate in the development of data portals and hubs, such as the Paciﬁc Marine Environmental Laboratory (PMEL) Climate Data Portal and the National Oceanographic Partnership Program (NOPP) Virtual Ocean Data Hub (Soreide et al. 2001). For further information, the UHSLC and BODC web sites (http://uhslc.soest.hawaii.edu and http://www.bodc.ac.uk, respectively) may be consulted. The ﬁrst set of WOCE CD-ROMs was released to coincide with the WOCE Scientiﬁc Conference in May 1998 (WOCE 1998). These were distributed to all scientists registered for the Conference and the Sea Level DACs distributed further copies to their data suppliers. Version 2.0 of the WOCE Global Data CD-ROMs (WOCE 2000) were published in September 2000, and have been widely distributed to the WOCE Community. Again, copies have been forwarded to the sea-level data suppliers, GLOSS contacts and other sea-level scientists. In addition to the datasets from the WOCE Sea Level DACs, the CD-ROM includes a tidal constituents dataset produced by LEGOS, Toulouse, the Permanent Service for Mean Sea Level (PSMSL) monthly and annual mean dataset and the GLOSS Station Handbook. There are also links back to the web pages of each DAC so that recently acquired data can be accessed. A ﬁnal version of the WOCE Global Data CD-ROM set is due to be published in 2002.

The Global Sea Level Observing System (GLOSS) programme The WOCE sea-level activities took place alongside those of the GLOSS programme of IOC. GLOSS was proposed in the mid-1980s by Dr David Pugh (of what is now the Proudman Oceanographic Laboratory) and Professor Klaus Wyrtki (of the University of 14

SEA- LEVEL RESEARCH FRO M T I D E G A U G E S

Hawaii) as a means of ensuring the long-term provision of worldwide sea-level information from tide gauges to the PSMSL and to international oceanographic programmes such as WOCE (IOC 1990, Woodworth 1991, 2000). The major component of the programme is the GLOSS Core Network (GCN), the island subset of which corresponds closely to that of the WOCE network, and which is now over two-thirds complete. During the 1990s, a number of important developments took place in the sea-level ﬁeld, including the provision of precise altimetric and geodetic information and requirements for real time sea-level data, with the result that GLOSS priorities had to be re-assessed and a new Implementation Plan constructed (IOC 1998). The programme now contains particular components called GLOSS-OC, for the monitoring of aspects of the ocean circulation, and GLOSS-ALT, for the ongoing calibration of altimeter missions (p. 16), both of which owe their origins to experiences during WOCE. A further component called GLOSS-LTT is concerned with the provision of long-term sea-level change information for climate studies, with particular overlap with the objectives of the PSMSL. GLOSS developments have complemented well those of the WOCE programme. For example, GLOSS initiated a special Southern Ocean sea-level centre at the ANTF, based in Adelaide, which works closely with the two WOCE DACs (http://www.ntf.ﬂinders.edu.au/). GLOSS has played major roles in the training of scientists and technicians from many countries and in disseminating advice on requirements for modern sea-level measurements. Most new GLOSS-related tide gauges are now recommended to be based on the acoustic (Porter & Shih 1996) or pressure (Woodworth et al. 1996c) techniques rather than the conventional stilling well method. In addition, near real-time recording is becoming a priority for several oceanographic applications, including assimilation of tide gauge and altimeter data together in ocean models. IOC (2000a) provides a review of technical developments in tide gauges.

Validations of tide gauge and altimetric sea-level data and numerical model simulations by data intercomparisons A large number of papers can be found in the literature which include comparisons of altimetric sea-level data with tide gauge information. These comparisons were usually performed in order to provide spot-conﬁrmations of the precision of altimetric time series prior to further analysis of the altimetry for large-scale studies such as El Niño variability. A review of early comparisons, mostly in the tropical Paciﬁc and using Geosat data, can be found in Picaut & Busalacchi (2001). Two which may be referred to include Mitchum (1994), who compared tide gauge and TOPEX altimetry leading to the calibration studies (p. 16), and Verstraete & Park (1995) who conducted one of the few studies in the Atlantic Ocean. Harangozo et al. (1993) performed comparisons between T/P data and monthly mean values of sea level from the PSMSL database, pointing towards the eventual use of altimeter data in studies of long-term sea-level change. Most studies tended to conﬁrm the accuracy of a single altimeter sea surface height measurement as being typically 4 –5 cm (in the case of T/P, slightly worse for ERS), which was a level of accuracy anticipated from knowledge of the error budget of the altimetric system. A second valuable application of tide gauge sea-level data is in the validation of ocean model simulations related to ocean circulation and climate change studies, as outlined in 15

P . L. WOODW ORTH, C. LE PROVOST , L . J. R I C K A R D S , E T A L .

Section 5 of the GLOSS Implementation Plan (IOC 1998). Examples of such applications are more numerous now than at the start of the WOCE programme because ocean numerical models are more realistic, and ocean modellers consider sea-level ﬂuctuations to be an important physical quantity of interest for their studies. The report of the Honolulu Sea Level Workshop (NOAA 1998) contains several examples of such applications, including the review report of Gornitz (1998) which refers to Tokmakian (1996) for global ocean model experiments and Enﬁeld & Harris (1995) for tropical Paciﬁc simulations including data assimilation. Koblinsky in NOAA (1998) also discusses another major set of validation and application to ENSO (El Niño Southern Oscilation) modelling and prediction. Related studies include those of Fukumori et al. (1998), Ji et al. (1995) and Xue et al. (2000). A number of other studies of the use of tide gauge data in model development can be found in the literature. An example is that of Ezer et al. (1995) who investigated the interpentadal variability of the mid-latitude North Atlantic Ocean, based on short-term model simulations but with high resolution, and simulated the state of the Atlantic during 1955–59 and 1970–74. Results agreed with earlier studies, indicating that the Gulf Stream was considerably weaker (by about 30 Sv) during the 1970s, and also suggested changes in poleward heat transport. Tide gauge data from 15 stations along the North American coast were used to validate the model information and to conﬁrm that the modelled climate changes were realistic. A further point to make with regard to the symbiosis between models and data concerns the use of models for the design and improvements of sea-level gauge networks, making use of the physical insight provided through the models. The review report of Gornitz (1998), and discussion in the present paper, point to the fact that for monitoring of the global ocean circulation one cannot rely completely upon altimetry and that gauges are required in certain identiﬁable areas. Such areas can best be identiﬁed through model experiments. A second example can be taken from the related ﬁeld of long-term sea-level changes caused by climate change. Although the results of General Circulation Models are not in full agreement, they all indicate that sea-level changes will not be the same everywhere over the global ocean, and that enhanced signals may occur in areas such as parts of the North Atlantic or around Antarctica (Warrick et al. 1996, Church et al. 2001). This points to the need to include in any new sea-level gauge implementation plan the installation of new stations in these areas. Such choices, involving perhaps considerable expenditure, have to be optimised using the best possible sources of model information. The encouraging correspondence between tide gauge and altimeter data and numerical model information led to increasing conﬁdence in the ability of a subset of the global tide gauge network to provide an ongoing calibration system for altimetry, even the most enthusiastic supporters of which had come to realise contains systematic instrumental errors comparable to the ∼1 mm yr−1 signals of interest in studies of long-term sea-level change. This topic is discussed in the following section.

Altimeter calibration using WOCE tide gauges All altimeter missions require some form of calibration as a check on the performance of the radar and associated hardware. This is especially true for T/P and JASON-1, which have 16

SEA- LEVEL RESEARCH FRO M T I D E G A U G E S

the highest accuracy requirements for application to topics such as global sea-level change. This calibration is the analogue of what is known as datum control in tide gauge operations, and maintenance of the datum is the most critical factor in making long tide gauge records useful for climate studies. Similarly, maintaining the altimetric datum will be especially critical when one has access to a multi-decadal altimetric record obtained by combination of time series from a succession of altimeter missions. A ﬁrst question is, which datasets are the most suitable for comparison with the altimetry to perform the calibration? These data will clearly take the form of height measurements of “targets” by both the altimeter and by in situ methods. Several types of target have been experimented with in the past including active devices such as radar transponders and passive targets such as large lakes monitored by water level recorders. However, it happens that the most convenient, direct and permanent calibrations of ocean altimetry can be performed using the surface of the open sea itself near to tide gauges. Mitchum (1998) showed that one can make powerful calibrations of altimeter data (called “relative” calibrations in altimeter terminology) by comparing simply the time series of altimeter information obtained near to a tide gauge with the time series of sea-level data obtained from the gauge itself, without concern for any overall unknown systematic error or bias in the differences between the time series. In the early years of the mission, the TOPEX altimeter data were found to possess a bias (the difference between sea surface height measured by the altimeter and in situ systems determined from “absolute” calibrations, see below) of the order of 17 cm, and relative calibrations of the type conducted by Mitchum (1998) suggested a time-dependent error in the altimeter measurements, such that unrealistic determinations of global and regional sea-level change were obtained. Subsequent analysis showed these ﬁndings to be due to an algorithm error in the software used to compute T/P altimeter range. After the error had been corrected, the absolute bias reduced essentially to zero (which had been the bias obtained for POSEIDON altimeter since the start of the mission) and, as a consequence of the relative calibration by means of tide gauges, estimates of global sea-level change for the mission were signiﬁcantly reduced (Chelton et al. 2001, Nerem & Mitchum 2001). This demonstration of the continuing value of gauges was received with some amusement by sections of the tide gauge community and with embarrassment by altimetry overenthusiasts. However, in reality the experience provided conﬁrmation of the need to pursue the development of a combined gauge-altimeter global sea-level monitoring system. Mitchum (2000) has since developed a more sophisticated version of his earlier method, which was based upon a repeat-track analysis of the altimeter information and subsequent comparison with gauge data, to include consideration of land movements at gauge sites. He showed that relatively ad hoc estimates of land movements are adequate for global sea-level change studies (resulting in a residual error of order 0.4 mm yr−1 for the T/P mission duration, which is considerably lower than uncertainties from other sources), although information on land movements from techniques such as the Global Positioning System (GPS) or the Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) system would clearly be desirable if possible. Relative calibrations of the Mitchum type have been conducted independently by other authors (Chambers et al. 1998, Moore et al. 2000). Murphy (1998) applied a similar method to time series obtained from missions with long or non-repeat ground tracks. An “absolute” calibration differs from the “relative” method in that one attempts to relate the absolute value of the altimeter measurement of sea surface height to tide gauge sea-level 17

P . L. WOODW ORTH, C. LE PROVOST , L . J. R I C K A R D S , E T A L .

data that have been located in the same geocentric reference frame using an advanced geodetic system such as GPS (see p. 25). The absolute calibration component of altimeter bias in most missions to date has been undertaken at dedicated sites such as Bermuda (for GEOS-3 and Seasat), Harvest and Lampedusa (T/P), Venice Tower (ERS-1), Newhaven/ Herstmonceux (T/P and ERS-1) which have a tide gauge, the data from which are located in the same geocentric reference frame as the altimeter data by means of laser ranging and GPS connections between laser and gauge. In these early examples, the laser had two functions. First, to determine the geocentric co-ordinates of the gauge, the GPS being useable for differential laser-gauge connections only; and second to enable short-arc precise orbit determination for the area of the test site, the global orbits provided by data centres being too poor for calibration purposes. However, nowadays GPS itself is capable of providing geocentric co-ordinates to the centimetre level, and the accuracy of the global orbits obtained from near-continuous satellite tracking by GPS and DORIS approaches that calculable by short-arc determinations (p. 25). Consequently, many gauges equipped with GPS at ocean islands and along continental coastlines could, in principle, be used for absolute calibration, as long as the distance between gauge and altimeter measurement point is not large (a few 10 s km being ideal) and as long as precise estimates of geoid-difference are available from a local gravity model. If continuous (or repeated) GPS measurements are made alongside the gauge, then vertical land movements due to geological processes will be automatically accounted for. An alternative form of absolute calibration, which has been investigated by several groups, is the use of an ocean buoy equipped with a GPS receiver (or at least a GPS antenna if the receiver is located on a boat separate from the buoy itself ), which then functions as both the gauge and GPS, and allows for the direct over-ﬂight of the satellite without the need for geoid-difference corrections. However, while such GPS-buoys might be useful in dedicated experiments, they lack the critical quality of permanence that is provided by a conventional coastal tide gauge. One can imagine several different strategies for long-term calibration of a set of altimeter missions over many years. Those contrived by participants in the upcoming JASON-1 mission have been described by Haines & Ménard (2000). The ideal strategy is obviously ongoing absolute calibration. However, a more practical alternative is probably the use of individual, precise relative calibrations using gauges for the length of each mission, but with some form of absolute calibration performed at the start of, and to some extent throughout, the mission. This is the approach adopted by the community to date and which will probably be employed for the near future. Whichever particular approach is chosen, it is clear that a subset of the global network of gauges (a version of GLOSS-ALT) will continue to be a major component of the calibration system.

Bottom pressures and ACC choke points Bottom pressure recorders have been used for many years to measure ocean tides in the deep ocean and, more recently, non-tidal barotropic changes in the water column resulting from ocean circulation changes. Spencer & Vassie (1997) and Cartwright (1999) provide histories of the development of the technique.

18

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BPRs have an advantage over conventional coastal gauges in that they can be deployed in almost all parts of the ocean at all but the greatest depths (i.e. to depths of approximately 5000 m), including high latitude regions where conventional gauge operations may be difﬁcult due to ice cover or other environmental conditions. They provide continuous deepocean measurements, without the aliasing problems of altimetry (Gille & Hughes 2001). On the other hand, a continuous BPR record cannot be obtained from a single deployment for more than a few years (4 yr being the longest to date from the POL MYRTLE device, Spencer & Foden 1996, Spencer & Vassie 1997), while the data sometimes contain a longterm instrumental drift (“transducer creep”) and cannot be related to a geodetic datum. It has to be recognised that bottom pressure is a different variable to surface sea level, because the latter can contain contributions from baroclinic changes in the water column. Bottom pressure is usually more useful for circulation studies because ocean modellers normally require pressure information rather than sea level. On the other hand, it means that a time series from a deep ocean BPR cannot be expected to correspond in all respects to a series of sea-level measurements by an altimeter. BPRs were considered for deployment at ACC “choke point” sections during WOCE CP-2 due to the success of Drake Passage bottom pressure measurements during the International Southern Ocean studies (ISOS) programme which spanned 1977–82 (Wearn & Baker 1980, Whitworth & Peterson 1985), and to the lack of established conventional coastal gauges in the region, a situation that is now improved as a result of GLOSS developments. From the ISOS BPR and current meter datasets, Whitworth & Peterson (1985) determined the rms variability in the transport through the Passage to be of the order of 10 Sv and to be primarily barotropic. They also recorded two examples of transport ﬂuctuations approaching 50% of the mean (or approximately 50 Sv) over periods as short as two weeks. The 10 Sv corresponds to approximately 5 mbar (or 5 cm) pressure difference if barotropic ﬂow is assumed, which means that the changes are at the limit of measurement by altimetry, aside from any considerations of sampling. From Whitworth & Peterson (1985) and from later modelling (Woodworth et al. 1996b, Hughes et al. 1999), we know that most of the pressure (or sea level) changes which are associated with the transport changes take place at the southern side of the Passage. (For other studies of monitoring Drake Passage ﬂows using altimetry alone, see Challenor et al. 1996 and Challenor & Tokmakian 1999.) BPR measurements in the Drake Passage were recommenced during WOCE by UK groups (Spencer et al. 1993). The rms variability of Drake Passage transport was observed to ﬂuctuate from year to year (Meredith et al. 1996). For example, rms of 5.3 Sv was recorded in 1993 compared with 8.9 Sv in 1990 (after application of a 10-day ﬁlter). All years 1989–94 demonstrated lower variability than observed by Whitworth & Peterson (1985) and no evidence was found for large ﬂuctuations of several 10 s of Sv. Hughes et al. (1999) concluded that the transport variability on timescales of 10–220 days was consistent with that of a barotropic mode following f/H contours, with the south side of relatively greater importance for monitoring changes in transport. Gille et al. (2001) employed BPR, T/P, zonallyaveraged wind and numerical model information and concluded that barotropic transport and wind forcing are coherent over timescales of approximately 10–256 days at the Drake Passage, with barotropic transport lagging wind forcing by about 1/18 of a cycle for a broad range of frequency, suggesting the ocean response to wind is controlled by both a “tendency term” (an along-stream average current that balances wind stress ﬂuctuations) and a frequencydependent viscous process.

19

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Woodworth et al. (1996b) demonstrated, by comparing data from coastal and deep-sea instruments, that the accuracy of T/P altimetry in the Drake Passage area is similar to that obtained for other parts of the world. In particular, the BPR and T/P data from the northern side of the Passage were found to be in close agreement. On the other hand, the southern side information indicated qualitative similarities to, but large quantitative differences between, altimetric SSP and in situ bottom pressure, a part of which was explainable from baroclinic variability around a known, topographically-trapped “cold core eddy” centred at approximately the location of the MYRTLE BPR. This emphasises that BPR deployments have to be chosen with care to optimise the measurement of the barotropic processes of interest, because baroclinic corrections to the pressure data can be large and imprecisely known. Rubython et al. (2001) provide a full discussion of the bottom temperature data from the MYRTLE position, and of the repeat hydrographic information obtained nearby, with particular emphasis on a 0.1°C cooling which occurred during 1994–95 and which has been linked to variability in Weddell Sea Deep Water. Baroclinic ﬂuctuations in mid-Passage were previously studied using inverted echo sounders (IESs) by Meredith et al. (1997) with the same group of IESs deployed also to monitor Denmark Strait overﬂow (Dickson et al. 1999). In addition to the Drake Passage, deployments have taken place at the Africa–Antarctica and Australia–Antarctica choke points by US groups and Amsterdam–Kerguelen by UK/ French groups. An early analysis of the temporal variability of the ACC transport between Amsterdam and Kerguelen was made by Vassie et al. (1994). Combined analyses of all the separate choke point datasets are still in progress. However, it is clear that the use of “south side” gauges, whether BPRs or conventional gauges on the Antarctic coast, as a monitor of ACC transport variability is a potentially important research topic for both tide gauge specialists and ocean modellers. Woodworth et al. (1999) demonstrated the remarkable coherence of subsurface pressure (SSP), or “inverse barometer corrected sea level”, measured at locations around Antarctica for timescales of 10 days and longer, which suggests that a small number of high-quality Antarctic gauges, whether located at the “choke points” or not, might be adequate for providing the required ongoing information on the strength of the ACC which cannot be provided by altimetry. On longer timescales, gauge records are required in Antarctica to contribute to an understanding of the interesting ocean-atmosphere dynamics associated with the Antarctic Circumpolar Wave (Jacobs & Mitchell 1996) and other features. BPRs have also been employed during WOCE to study the spatial scales of the coherence of barotropic ocean variability, with bottom pressure change in ocean sub-basins found to be coherent over large areas (Luther et al. 1987, Filloux et al. 1991, Chave et al. 1992, Woodworth et al. 1995, Hughes & Smithson 1996). Knowledge of the spatial scales of the coherence is of particular importance to the measurement of changes in the distribution of ocean mass by forthcoming space gravity missions. It also opens up the possibility for future ocean circulation models to be constrained by sea-level information from altimetry at the surface, together with data on ocean mass changes integrated throughout the water column, arising from measurements of space gravity (NRC 1997, Wahr et al. 1998). All BPR data contributed to WOCE are included in the ofﬁcial CD-ROM sea-level products for the programme and can also be obtained from the Global Undersea Pressure (GLOUP) dataset managed by the PSMSL on behalf of the International Association for the Physical Sciences of the Ocean Commission on Mean Sea Level and Tides (IAPSO CMSLT). 20

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Other uses of tide gauge data during the WOCE period Sea-level data were employed during WOCE to monitor ﬂows through straits at locations other than the Southern Ocean. One group of papers concerned the Indonesian ThroughFlow. Arief & Murray (1996) investigated the particular relationship between Cilicap sea level and ﬂow through Lombok Strait, which accommodates approximately a quarter of the throughﬂow, and concluded that Cilicap could be used to predict ﬂows with a small lead. Molcard et al. (2001) compared 20 m depth currents in Ombai Strait with upstream sea-level data from Benoa during 1995–96 and concluded that Benoa level might be useable (given better quality gauges) as a proxy for the current. Bray et al. (1996) conducted a wider-area study combining sea-level and thermocline information and concluded that interannual variability in the throughﬂow depends on changes in deep as well as surface ﬂows. Potemra et al. (1997) studied the large-scale pressure gradient forcing of throughﬂow using T/P data (previously validated using tide gauges) and found that the throughﬂow is controlled by sealevel changes on the Paciﬁc side on interannual timescales and by a combination of Indian and Paciﬁc Ocean processes on seasonal to annual timescales. Susanto et al. (2000) found that the intraseasonal (1–2 months period) variability of sea level in the Makassar Strait was a response to remotely forced Kelvin waves from the Indian Ocean progressing through Lombok Strait, together with Rossby waves from the Paciﬁc Ocean. These several examples clearly demonstrate the importance to the international oceanographic community of good quality tide gauges located at strategic positions to monitor the Indonesian Through-Flow. Several recent papers have been concerned with aspects of tidal and long-term exchanges through the Strait of Gibraltar (Ross et al. 2000, Tsimplis 2000, Tsimplis & Bryden 2000). One conclusion is that, while gauges can be used to monitor the Atlantic-inﬂow and Mediterranean-outﬂow exchanges to ﬁrst order, other information is also needed on the position and slope of the interface between Mediterranean and Atlantic waters across the Strait in order to estimate transports reliably. In the Mediterranean itself, Tsimplis (1997) used sea-level data from each end of the Strait of Euripus to investigate tidal and non-tidal ﬂows through the strait. Clarke & Ahmed (1999) used data from six locations along the South American coast from Peru to Chile to investigate intraseasonal oscillations of sea level, observing poleward propagation at these timescales. Flows along coastlines were also investigated by McClimans et al. (1999) who employed bottom pressure and coastal sea-level data to monitor a shelf edge current, in this case a predominantly barotropic current along the Norwegian continental slope. Sea-level changes and currents along the coast of India and their relationship to monsoon rainfall and subsequent reduction in salinity of coastal waters were studied by Shankar & Shetye (1999) and Shankar (2000), while the relationship of sea levels in Bangladesh to the Southern Oscillation was considered by Singh et al. (2001). WOCE data were also used to test aspects of large-scale ocean dynamics. Prior to the availability of T/P altimetry, Kawabe (1993, 1994) made extensive use of Paciﬁc island tide gauge data to investigate the interannual variability of equatorial sea level related to El Niño with the role of equatorial Kelvin and Rossby waves parameterised within a two-layer reduced-gravity model. Also prior to the availability of copious, precise altimeter data, Unal & Ghil (1995) studied interannual and interdecadal oscillation patterns of sea-level change using 213 tide gauges, emphasising the worldwide distribution of quasi-biennial and lowerfrequency (period 4–5 yr) ENSO-related variability in the records, in addition to secular trends. Merriﬁeld et al. (1999) employed regression analysis of tide gauge and T/P data in 21

P . L. WOODW ORTH, C. LE PROVOST , L . J. R I C K A R D S , E T A L .

the Paciﬁc for the period for which both data types were available to construct Empirical Orthogonal Function (EOF) patterns of temporal and spatial variability connected with the ENSO cycles. The EOF patterns of change were then applied to the longer historical record from gauges alone to assess island regions most at risk of anomalously high sea levels during peak El Niño and La Niña events. Johnston & Merriﬁeld (2000) studied the time dependence of the North Equatorial Countercurrent and South Equatorial Current in the western Paciﬁc during 1975–97, and its relation to ENSO events, using a combination of tide gauge and T/P data. Sturges et al. (1998) and Hong et al. (2000) successfully related the sea-level changes observed at Bermuda and along the US Atlantic coast to basin-scale changes in the wind ﬁeld through parameterisations of gyre-scale dynamics. Sturges & Hong (2001) differenced sea levels measured on the US east coast by gauges from those calculated for the offshore side of the Gulf Stream by an ocean model and showed that the differences agreed well at low frequencies with measured transports. Ezer (2001) tested this hypothesis using a more sophisticated Atlantic model forced by observed surface data, showing that variations in sealevel difference between ocean and coast are indeed coherent with Gulf Stream variations for periods shorter than 1 yr or longer than 4–5 yr. In the Paciﬁc, sea-level data from the coast of Japan were used extensively during WOCE to demonstrate and monitor the interannual and interdecadal variability of Kuroshio transports and meanders (Kawabe 1995 and references therein). Senjyu et al. (1999) discussed interannual variations throughout Japan with the ﬁrst EOF of the variability representing coherent changes around the entire coast, and the second mode resulting from large Kuroshio meanders. Several authors including Ponte (1997), Gaspar & Ponte (1997, 1998), Ponte & Gaspar (1999), Woodworth et al. (1995) and Mathers & Woodworth (2001) investigated dynamical violations of the “inverse barometer model” throughout the world ocean on a range of timescales with the use of tide gauge and altimeter data and numerical models, ﬁnding closer agreement with the simple model at longer timescales, but pointing to dynamical violations forced by winds and air pressure changes on timescales of days to weeks. The study of long-term change in global sea level did not fall within the particular remit of the WOCE programme. Nevertheless, the study beneﬁted in several ways from the development of the international sea level network during WOCE. A ﬁrst example concerns the provision of calibration information to the ﬁrst determinations of truly global sea-level change from altimetry (Nerem & Mitchum 2001). A second concerns the establishment or refurbishment of a number of island gauges in both hemispheres that have records one or two decades long. These will in time provide a special set of deep ocean secular trends in sea level, which may be more representative of oceanic change than trends measured at continental coastlines so far. A third derives from a fuller understanding of the ocean from WOCE in general and its role in climate and sea-level change (i.e. essentially WOCE Goal 1). Recent reviews of research into long-term sea-level changes and their causes, including the role of the ocean circulation, can be found in Raper et al. (2000) and Church et al. (2001). Similarly, the study of variability in sea level at different frequencies cannot strictly be considered as part of WOCE. Nevertheless, there is considerable overlap. Research in this ﬁeld was reviewed by Woodworth (1993). Of more recent European research, Tsimplis & Baker (2000) and Tsimplis & Josey (2001) have studied changes in European and Mediterranean sea levels associated with the North Atlantic Oscillation (NAO), while Tsimplis et al. (1994) and O’Connor et al. (2000) have investigated causes of the North Sea “pole tide” 22

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including the role of the Atlantic wind ﬁeld. Each of these can be related to forcings (e.g. wind stress) which in turn will relate to changes in regional ocean circulation. Tsimplis & Woodworth (1994) described the global distribution of the seasonal cycle of sea level using worldwide tide gauge data while Plag & Tsimplis (1999) investigated interannual variations in the northern Europe sea level seasonal cycles.

Deep ocean tide model developments during WOCE The development of a new generation of deep ocean tide models during WOCE was a major achievement of satellite altimetry, and in particular of the T/P mission (Le Provost et al. 1995a, Andersen et al. 1995, Woodworth et al. 1996a, Shum et al. 1997, Ray & Woodworth 1997, Le Provost 2000). In this section, we provide a brief overview of the way the gauge data have contributed to these model developments. Tide gauges have proved to be important in global tidal studies in two main ways. The ﬁrst is in the provision of in situ information by which one can validate the altimetric information, including the provision of comprehensive accuracy assessments. The second stems from the fact that altimetric sampling is not well matched to the needs of tide model development near the coasts. One has to assimilate conventional gauge (or BPR) tidal data into the models along with the altimetry in order to provide the required densiﬁcation of tidal information along shorelines. In 1995, the T/P SWT conducted a major assessment of a large number of new global ocean tide models which had become available largely as a result of the superb accuracy and coverage of T/P data and of progress in numerical modelling. The primary reason for the study was to provide the SWT with a recommendation on which models to use for future altimetric analyses. However, as ocean tides play a major role in many areas of geophysics, it was felt that such an assessment would also be useful to the wider community. That report was published as Shum et al. (1997). Before the T/P era, two main global ocean tide models had been used by researchers, those of Schwiderski (1980a,b) and Cartwright & Ray (1990). The Schwiderski model was constructed by means of a hydrodynamic interpolation scheme for the assimilation of the tidal constants dataset derived from the global collection of tide gauge data. That model, although now known to contain decimetric and larger errors, played a central role in oceanographic and geophysical research for more than a decade. Geosat in the late-1980s provided the ﬁrst copious altimetric dataset for extended global tide studies and enabled the derivation of models of comparable or better accuracy than Schwiderski (e.g. Cartwright-Ray). However, it was not until 1995, when almost three years of T/P data had become available, that really signiﬁcant improvements in this ﬁeld started to emerge. The new models were computed in different ways. Some were purely empirical solutions derived from T/P data, somewhat analogous to Cartwright-Ray, although analysis details were different in each case. Others differed in being the results of different forms of sophisticated data assimilation into numerical models wherein hydrodynamic constraints effectively act as a ﬁlter on and an interpolator between the data. Preliminary evaluations and comparisons of all the new models showed that they were very similar, with barely a centimetre or two difference between them in most parts of the world. Therefore, a set of tests was constructed, one of the most important of which comprised comparisons of tidal parameters derived from the models to those obtained from pelagic and island tide gauge data. 23

P . L. WOODW ORTH, C. LE PROVOST , L . J. R I C K A R D S , E T A L .

For the Shum et al. (1997) comparisons, 49 island and 53 BPR gauge records were employed. The tidal harmonic constants from these 102 sites were compared with those from the new models, and root-mean-square (rms) differences for each constituent were computed. Most new models were seen to have residual rms values of less than 2 cm for the main lunar component M2, and an overall root-sum-square (rss) derived from the 8 major constituents of the order of 2.5–3.0 cm. This was was a signiﬁcant improvement on the Schwiderski and Cartwright-Ray models (4 cm and 3 cm rms for M2 and 4.8 cm and 4.6 cm overall rss, respectively). One of the primary objectives of this study was to select two of the best models for future reprocessing of T/P data, because future work could not be done with many models. One of the original speciﬁcations for the selection had been that one of the two models should be a pure hydrodynamic model and that the other should be based primarily on T/P data. However, in practice that choice was not possible as the nearest candidate pure hydrodynamic model FES94.1 (Le Provost et al. 1994) was not accurate enough. Consequently, after considerable discussion, the choice was made to select CSR3.0 (Eanes & Bettadpur 1996), as primarily a T/P derived model but with hydrodynamic model information content, and FES95.2 (Le Provost et al. 1998), as primarily a hydrodynamic model but with T/P information content. These two models could in some sense be said to approach an optimum model from different directions, and have since been used extensively by researchers of T/P data during the last 5 yr. Since then, after almost a decade of T/P and ERS observations, a new generation of models has been developed, which are incremental improvements on the ones produced in 1995. Taking advantage of the longer altimeter time series, the empirical models have gained in resolution in the frequency domain (better de-aliasing) and in space, mainly over shallow waters by using smaller bins for constructing the altimeter time series to be analysed (Ray 1999). Along-track tidal analysis has also become more reliable, and the results have been assimilated in hydrodynamic models (Matsumoto et al. 2000, Tierney et al. 2000). In addition, assimilation of tidal information from tide gauge data has been developed at the global (Lefèvre et al. 2000) and regional (Matsumoto et al. 2000) scales. These new solutions have improved upon those of the mid-1990s only marginally over the deep ocean (i.e. there has been little improvement on the deep-ocean 2 cm rms for M2 referred to above) but there has been signiﬁcant improvement in shallow water areas. Dorandeau et al. (2000) demonstrated this improvement by means of a comparison of these solutions with a set of 739 tide gauges selected by Lefèvre et al. (2001). The residual rms values for M2 in the shallow water areas are reduced from approximately 17 cm for CSR3.0, to 14 cm for the GOT99 model (Ray 1999), to <11 cm for FES99 (Lefèvre et al. 2001) (although in the latter solution, some tide gauge data were assimilated). These new models improvements have occurred mainly thanks to the availability of T/P and ERS altimeter data. However, the latitudinal coverage of these satellites is limited to 66° and 82°, respectively. For higher latitudes, and especially for the Arctic, the information from the few available gauges along the Antarctic and Arctic coasts are thus of major interest, and unique. FES99 is the only global tidal model that has assimilated these data. (See Smithson et al. 1996 and Lyard 1997 for discussions of regional polar models.) From this short review on the progress observed in tidal modelling over the last decade, it is clear that knowledge of the uncertainties in tide gauge tidal constants used for model accuracy evaluation is essential, and especially for their assimilation in hydrodynamic models. A systematic determination of these uncertainties was undertaken for the WOCE 24

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tide gauge dataset by Ponchaut et al. (2001), the results for which were included on one of the WOCE CD-ROMs (WOCE 1998). A ﬁnal tidal topic to be referred to concerns the internal, rather than the barotropic, tides. The ocean area adjacent to the Hawaiian islands comprises a region of steep bathymetric variations and sizeable impinging barotropic tide and is consequently an ideal laboratory in which to conduct studies. Ray & Mitchum (1996) used T/P data to determine the surface expression of the internal tide generated near to Hawaii, with its typically centimetric amplitude superimposed upon the longer wavelength incoming barotropic tidal signal. Tide gauges have also been used to investigate internal tide generation, with the amplitudes of the major constituents recorded by gauges at the islands observed to undergo low-frequency variability (order of 1 cm) in response to changes in the pycnocline of the neighbouring deep ocean (Ray & Mitchum 1997, Mitchum & Chiswell 2000).

Geodetic developments during WOCE In addition to the advances in satellite altimetry, considerable progress was made during the WOCE ﬁeld programme in the development of new geodetic techniques, primarily the US GPS but also the French DORIS system. These techniques are required to provide precise geocentric positioning of tide gauge benchmarks and, over periods of typically a decade of continuous (or repeated) monitoring, of rates of vertical movement of the marks. Geocentric co-ordinates of the benchmarks are required if the tide gauge measurements are to be located within the same global geodetic reference frame as altimeter data. As the benchmarks will move over time for geological reasons, continuous (or repeated) geodetic measurements are required. Other advanced methods such as absolute gravity are now also accurate enough to detect these vertical crustal movements. Vertical land movements have been known for many years to be an important signal in tide gauge sea-level records (Emery & Aubrey 1991). However, it was not until the recent developments of the new geodetic techniques that it became possible to consider monitoring them. A series of workshops was held during the late 1980s and early 1990s under the auspices of the IAPSO CMSLT and other interested scientiﬁc groups, culminating in a major report on the state of the art following a workshop hosted by the International GPS Service (IGS) at the Jet Propulsion Laboratory in 1997 (Neilan et al. 1998). Follow-up meetings were held in 1999 and 2001 and all working groups of the IAG, IAPSO, IGS, PSMSL and GLOSS have since been consolidated into one expert team that is responsible for providing advice to installers of GPS equipment at gauge sites (IOC 2000a). The GPS advances mean that it is now possible to locate the position of a benchmark in a geocentric reference frame to an accuracy of the order of 1 cm. The fact that sea level determined from the gauge will be in the same geocentric reference frame as the altimeter data means that absolute altimeter calibration is possible (see p. 16). In addition, if one has good knowledge of the regional geoid, geocentric positioning allows the vertical datums in neighbouring countries and continents to be related. In addition, positioning and geoid information enables the absolute, rather than time-varying, geostrophic ocean currents between geocentrically-located gauges to be calculated. Major improvements in knowledge of the geoid are expected in the next few years which will advance this ﬁeld considerably (Wahr et al. 1998, Balmino et al. 1999). 25

P . L. WOODW ORTH, C. LE PROVOST , L . J. R I C K A R D S , E T A L .

With regard to studies of long-term mean sea-level changes, continuous GPS measurements should be capable of measuring the vertical crustal movements at tide gauges with uncertainties of order 0.5 mm yr−1 in approximately a decade. These vertical crustal movements should then be separable from the climate related trends in the MSL data, which are of order 1–2 mm yr−1. Hence, relative sea-level trends determined from tide gauges can be converted to absolute sea-level trends, without having to rely on models of some of the geodynamic processes as is done at present. Although time series of vertical land movements are in most cases only a few years in duration, and computed rates of vertical land movement have as yet large uncertainties, the next decade should see the compilation of an extremely important set of land movement data with which to confront the geodynamic models. The French DORIS system is of particular importance, not only as a complementary technique for monitoring vertical crustal motion (Cazenave et al. 1999), but also as the main method by which all future planned altimeter satellites will be tracked. For GLOSS-ALT, tide gauges, GPS and DORIS should ideally be all co-located nearby. Absolute gravimeters have become much more portable in recent years and are now achieving accuracies of 1–2 microgals and are being used both at permanent space geodetic sites and near tide gauges. This accuracy is equivalent to 5–10 mm of vertical crustal movement. Since the acceleration of a mass in free fall is measured using metrological standards of distance and time, the absolute gravity observations provide a check on the long-term vertical crustal movements in GPS or DORIS that is completely independent of the error sources in space geodetic measurements. The ratio of gravity change to crustal movement also gives information on the physical mechanisms involved and can therefore be used to constrain the geodynamical models. Although tide gauge data were never employed as originally intended for altimeter orbit constraints during WOCE (p. 3), the two types of geocentrically-positioned data have been used together in optimal ways by several groups in order to construct regional mean sea surfaces. For example, in an early study, Houry & Mazzega (1991) conducted a synthesis of geodetically-corrected tide gauge data together with altimetry in order to determine combined MSS ﬁeld for the Mediterranean. Methods were generalized to small ocean basins by Fenoglio & Groten (1995).

The future role of the global tide gauge network for ocean circulation and climate studies Tide gauges have clearly played an important, if secondary, role to altimetry for sea-level measurements during WOCE. Consequently, it is important to ask how the global tide gauge network might evolve in future, now that the “age of altimetry” has arrived. This issue was discussed in the GLOSS Implementation Plan (IOC 1998) and, most recently, in Mitchum et al. (1999a,b). The aim is to have a global network which complements the altimetry as far as possible. To this end, we believe that there must be at least: •

A globally-distributed, carefully-maintained set of gauges capable of providing data in near-real time and with high frequency (hourly or better) to data centres for ongoing altimeter calibration. This set would be functionally similar to the GLOSS-ALT subset of IOC (1998). The use of such data would beneﬁt from 26

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•

•

•

•

•

having a long historical record from each site, in order to learn as much as possible about sea-level variability at each location. In addition, there is a need for space geodetic measurements (i.e. GPS or DORIS) near to the gauges in order to correct for long-term vertical land movements and to position the gauge data into the same geocentric reference frame as the altimeter data. Ocean islands are the preferred locations for such a set, although gauges on continental coastlines may also be employed if their data are found to represent reasonably well the open ocean sealevel variability observed by the altimeter (e.g. Murphy et al. 1996). A set of gauges or gauge-pairs at straits (e.g. Florida, Gibraltar, Drake Passage), the transports through which are best measured by means of gauges rather than by altimetry, and at certain other locations (e.g. western boundary current coastlines, see Gornitz 1998). This set would be functionally similar to the GLOSS-OC subset of IOC (1998). A set of gauges at higher latitudes or in other environmentally-hostile areas for which the application of altimetry to ocean circulation studies is not optimal. Priority areas include the Antarctic coastline, and the Arctic where the operation of a number of gauges has recently been terminated (IOC 2000b). The continuation of sea level recording at established sites with long historical records for application to the study of long-term global sea-level change. This set would be functionally similar to the GLOSS-LTT subset of IOC (1998). It will be necessary to monitor vertical land movements at such gauges by means of GPS and other geodetic techniques. Regional and national densiﬁcations of the global network for particular purposes. For example, the need for gauges at ocean islands under threat of sea level rise has been well-publicised (IOC 1994). The Coastal Module of the IOC Global Ocean Observing System (GOOS) and the wider needs of operational oceanography have many requirements for gauges and for the numerical models that assimilate the gauge data (e.g. Flather 2000) which will differ between regions. The continuation of the high-quality T/P altimetric time series into the JASON-1 mission and beyond, which will provide the accuracy necessary for basin- and global-scale sea-level change studies, and which will supply the reference altimeter dataset with which other, less-precise altimeter datasets can be merged for studies across a range of spatial scales. This in turn implies the provision of a second or third altimeter satellite (e.g. Envisat) or potentially the development of alternative altimeter technology (e.g. swath altimetry).

Conclusions In parallel with the WOCE ﬁeld programme of the 1990s, there has been considerable development of global numerical models and, in particular, of models capable of assimilating the measurements. The success of the modelling activities led to the proposal for the second phase of WOCE called AIMS (Analysis, Interpretation, Modelling and Synthesis) which will continue to 2002 and which will have a signiﬁcant impact on follow-on programmes such as the WCRP Climate Variability and Predictability Programme (CLIVAR), and the Global Ocean Data Assimilation Experiment (GODAE) which is a major component of the IOC GOOS programme. Altimeter and tide gauge sea-level data have been demonstrated to be amongst the most useful of ocean parameters to be employed in such modelling and data assimilation activities, and their use into the WOCE-AIMS era and beyond is assured. 27

P . L. WOODW ORTH, C. LE PROVOST , L . J. R I C K A R D S , E T A L .

These developments conﬁrm that even in the “age of altimetry” there will continue to be major oceanographic applications which require access to reliable tide gauge sea-level datasets. The superiority to altimetry in terms of temporal sampling and suitability for particular geographical locations, together with the fact that tide gauge data are required to calibrate the relatively-new altimetric technique, means that high quality gauges will be required for many years to come. If one combines these ocean circulation requirements together with those for the ongoing monitoring of long-term global sea-level change, and with those for a vast range of coastal applications, it is clear that national and international funds must be made available for the continued enhancement of the global tide gauge network.

Acknowledgements We would like to thank the many tide gauge operators for providing the WOCE programme with the essential sea-level data. Sally Dowell, Kay Thorne and Philip Axe (BODC/POL) and Shikiko Nakahara and Fee Yung Porter (UHSLC) are thanked for their work for the WOCE Sea Level Centres. John Church, John Gould, Karen Heywood, Chris Hughes and Michael Tsimplis made valuable comments on a preliminary version of this paper.

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Whitworth, T. & Peterson, R. G. 1985. The volume transport of the Antarctic Circumpolar Current from bottom pressure measurements. Journal of Physical Oceanography 15, 810–816. Woodworth, P. L. 1991. The permanent service for mean sea level and the global sea level observing system. Journal of Coastal Research 7, 699–710. Woodworth, P. L. 1993. A review of recent sea-level research. Oceanography and Marine Biology: an Annual Review 31, 87–109. Woodworth, P. L. 2000. Monitoring and predicting long term sea level changes. Periodicum Biologorum 102, 665–672. Woodworth, P. L., Hughes, C. W., Vassie, J. M., Spencer, R., Whitworth, T. & Peterson, R. G. 1999. Coherence of bottom and sub-surface pressures around Antarctica. In Proceedings of the Workshop on Ocean Circulation Science derived from the Atlantic, Indian and Arctic Sea Level Networks, 10–11 May 1999, GRGS Toulouse, France. Intergovernmental Oceanographic Commission Workshop Report No. 171, 10–13. Woodworth, P. L., Shum, C. K., Le Provost, C. & Ray, R. D. 1996a. Signiﬁcant improvement in ocean tide models. International WOCE Newsletter, No. 24, 36–38. (Unpublished manuscript) Woodworth, P. L., Vassie, J.M., Hughes, C. W. & Meredith, M. P. 1996b. A test of the ability of TOPEX/POSEIDON to monitor ﬂows through the Drake Passage. Journal of Geophysical Research 101, 11935–11947. Woodworth, P. L., Vassie, J. M., Spencer, R. & Smith, D. E. 1996c. Precise datum control for pressure tide gauges. Marine Geodesy 19, 1–20. Woodworth, P. L., Windle, S. A. & Vassie, J. M. 1995. Departures from the local inverse barometer model at periods of 5 days in the central South Atlantic. Journal of Geophysical Research 100, 18281–18290. World Ocean Circulation Experiment (WOCE). 1988a. World Ocean Circulation Experiment implementation plan. Volume 1: Detailed requirements. WMO World Climate Programme Research Report WCRP-11, World Meteorological Organisation, Geneva, Switzerland. World Ocean Circulation Experiment (WOCE). 1988b. World Ocean Circulation Experiment implementation plan. Volume 2: Scientiﬁc background. WMO World Climate Programme Research Report WCRP-12, World Meteorological Organisation, Geneva, Switzerland. World Ocean Circulation Experiment (WOCE). 1989. Report of the International WOCE Scientiﬁc Conference, UNESCO, Paris, 28 November – 2 December 1988. WMO World Climate Programme Research Report WCRP-21, World Meteorological Organisation, Geneva, Switzerland. World Ocean Circulation Experiment (WOCE). 1994. WOCE Data Handbook from the WOCE Data Information Unit (second printing). WOCE International Project Ofﬁce, Southampton, UK, WOCE Report No. 120/94. World Ocean Circulation Experiment (WOCE). 1998. WOCE global data: sea level data, provided by the WOCE Data Products Committee. (Version 1.0). WOCE International Project Ofﬁce, Southampton, UK, WOCE Report No. 158/98. World Ocean Circulation Experiment (WOCE). 2000. WOCE global data: sea level data, provided by the WOCE Data Products Committee. (Version 2.0). WOCE International Project Ofﬁce, Southampton, UK, WOCE Report No. 171/00. Wunsch, C. 1991a. Global scale sea surface variability from combined altimetric and tide gauge measurements. Journal of Geophysical Research 96, 15053–15082. Wunsch, C. 1991b. Large scale response of the ocean to atmospheric forcing at low frequencies. Journal of Geophysical Research 96, 15083–15092. Xue, Y., Leetmaa, A. & Ji, M. 2000. ENSO prediction with Markov models: the impact of sea level. Journal of Climate 13, 849–871.

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Oceanography and Marine Biology: an Annual COASTAL AND SHELFSE A Review MO D E2002, L L I N40, G 37–141 © R. N. Gibson, Margaret Barnes and R. J. A. Atkinson, Editors Taylor & Francis

COASTAL AND SHELF-SEA MODELLING IN THE EUROPEAN CONTEXT J. E. JONES Proudman Oceanographic Laboratory, Bidston Observatory, Bidston Hill, Prenton, CH43 7RA, UK e-mail: [email protected]

Abstract Within the context of Europe, numerical modelling of coastal and shelf seas exhibits a range of diversity and a level of maturity reﬂecting global developments in this ﬁeld. Results from a detailed survey distributed to researchers across Europe, supplemented by a literature search, indicates the variety of models and modelling techniques that are presently used. An inventory of models is assembled. Each aspect of the hydrodynamics, the boundary conditions and included processes such as waves, biology and sediments, are investigated in turn. This study helps to identify areas where consensus, or lack of consensus, exist. Thus, possible future model developments that suggest future trends are indicated. Uniquely, the European context provides a forum that fosters collaborative projects including model inter-comparisons. A list of projects relevant to modelling is given. At present such projects help to deﬁne the performance of individual models. However, they may eventually lead to a convergence and rationalisation of numerical model schemes or the establishment of community models that address not just the physics but also the salinity, temperature, chemical and biological aspects of shelf seas. Present and future progress towards such goals is indicated. The desirability of rationalisation, as discussed in the literature, is assessed. Present and future modelling challenges are indicated.

Introduction A recent survey of “operational” modelling was carried out in response to a questionnaire sent out under the EuroGOOS collaboration (Prandle & Flemming 1998). It revealed some 25–30 models of the southern North Sea alone, with many more covering the whole or parts of the northwestern European Shelf, the Mediterranean and adjacent seas at various time and space scales. Some of these models had been developed from basic principles by European workers, others are developments of pre-existing models originating either in Europe, the United States or elsewhere. The question may be raised whether such a multiplicity of models is necessary, and if this diversity is likely to increase further. Could there in future be a trend towards rationalisation with just a few “standard” models being generally available? A natural further question may be “What are the best schemes available?” and ﬁnally, “Are there any problems that are not being presently addressed?” It is the aim of this review to examine these issues. On a global basis, shelf-sea modelling has reached a level of diversity and maturity where it is being taken up by researchers in an ever-increasing number of countries. Surveys 37

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(Moll & Radach 2001) show that the number of papers being published in this ﬁeld is accelerating rapidly as is the number of models, or at least different versions, of models. In the educational sphere each basic model may evolve different forms with every new student. For example, in just one academic year, BOM, the Bergen Ocean Model (Berntsen 2000) was used in four PhD theses (Eliassen 1999, Avlesen 2000, Eldevik 2000, Heggelund 2000), and one researcher reported the use of a particular model by over 60 students (K. George, pers. comm.). Incidentally, this source of new models must not be ignored because several widely used models have their origins in PhD theses. Consequently, with this ongoing proliferation it is now almost impossible to give a complete worldwide account of shelf-sea modelling. Nevertheless, a more restricted review of coastal and shelf-sea models in the European and adjacent shelf seas (Fig. 1a–e) is possible, because Europe may be regarded as a microcosm of the work being carried out globally. In Europe it can be argued that the diversity of modelling effort has arisen for geographical reasons because European waters are often ﬂanked by several nations, each of which may have its own modelling objectives. These objectives have evolved in response to the wide variety of shelf-sea environments surrounding Europe, from very high tidal ranges to virtually none, from high salinity to regions of freshwater inﬂuence and brackish zones, and a wide temperature range from Arctic ice to Mediterranean warmth. Some regions are very productive biologically, others less so and human impact through pollutants varies considerably. The European context provides an opportunity to examine how various shelf-sea environments are being simulated by a wide variety of models at the moment. The European context also has a unique characteristic in that through multinational projects, especially under the auspices of the European Union, different modelling techniques are being compared, and there is multinational collaboration on the development of new models and even internationally agreed operational models. These trends might reduce model diversity in the future. Nevertheless, it may be desirable to retain a wide variety of models, especially if the numerous European environments continue to provide new challenges for the future. This review will attempt to discern likely trends and identify future needs.

The need for modelling Hydrodynamic numerical modelling of shelf seas arose not only from scientiﬁc curiosity, but from a real need for the prediction of events such as storm surges. Storm surge numerical modelling schemes have been operational since the mid-1970s and have been constantly upgraded and enhanced (Bode & Hardy 1997, Flather 2000). This type of modelling falls within the remit of national agencies and research laboratories and may be regarded as a tool for the operational management of the seas. In the academic context, models are natural tools for scientiﬁc research in universities and research laboratories up to a preoperational stage. They can also provide a teaching function. A third area that requires model development is that of civil engineering where a good description of marine processes is required to underpin economic development and exploitation of the coastal seas. This arbitrary division of modelling into three areas is to a certain extent artiﬁcial as will be shown. Some models may straddle all three categories or move from one to another as they develop. 38

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Figure 1 a, northwest European continental shelf; b, Baltic Sea; c, European Arctic Ocean; d, western Mediterranean; e, eastern Mediterranean and Black Sea.

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40

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Source material for this review Even restricting interest generally to the European scene it is very difﬁcult to keep track of the wide variety of models that are available. For this review the source material is restricted to a literature search of approximately the last 4 yr of 35 journals that might be expected to report advances in modelling, although additional references have been sought where necessary. In addition a questionnaire was prepared, and itself reviewed, before being sent electronically to approximately 320 European researchers who had attended and contributed to workshops, conferences, symposia and other meetings at which papers in numerical modelling of coastal and shelf seas were presented. The questionnaire asked about the structure, hydrodynamics, boundary conditions, included processes, and applications of each model. Detailed information on approximately 100 models was obtained and compiled into spreadsheets. Some models were similar, being developments or applications of established models. Some were also models of speciﬁc processes such as waves, which may be components of larger scale models. However, as the spreadsheet was being compiled it became clear that, in some cases, there was a fairly general consensus on certain modelling components. For example, there was almost universal use of the Boussinesq approximation. In other cases, such as advection schemes, there were a wide variety of different methods in use.

Structure of review It is the intention of the main part of this review to demonstrate the diversity of models presently found in Europe. The approximate structure of the questionnaire will be followed and model details such as types of equations, methods of solution, discretisation, hydrodynamic features, boundary conditions and included processes will be examined. As the review proceeds, points of consensus or otherwise will be noted. If a certain feature is common to most models only exceptions to the general consensus will be discussed. If, however, there is no consensus on a particular model feature, this lack of agreement may indicate where further knowledge is required or that certain schemes are preferable for particular applications. It is also possible that a scheme is used purely for historical reasons. It is beyond the scope of this review to discuss in full technical detail all the various model components. Discussions of items such as turbulence methods ﬁll many books and papers. It is, however, the intention to demonstrate diversity where it exists. Following the survey, a discussion will attempt to answer the four questions posed in the introduction, to try to identify future trends in coastal modelling and future challenges, especially if they are not at present addressed.

Deﬁnitions “Coastal Modelling” is deﬁned to be the modelling of coastal and shelf seas excluding specialist topics such as beach processes, or river models that do not include a portion of the adjacent shelf sea. A model may be included if, in addition to coastal seas, it also covers a 41

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portion of the shelf edge and adjoining deep ocean. It is difﬁcult to exclude rigorously certain types of model as they may be, for example, of shelf edge processes that can inﬂuence processes on the shelf itself. In order to give a ﬂavour of the many types of models that are used in Europe no speciﬁc types are included or excluded. Ocean models which only deal with large-scale ocean circulation and which end at the shelf edge are, however, excluded. The “European Context” refers to models that have originated in Europe and are applied in European scenarios or they may have originated elsewhere but are nevertheless being applied by European researchers in European environments. Figure 1a–e shows the seas bordering the European coasts that are considered in this review. Those models which are used in a European scenario but which have originated outside Europe and are applied by researchers from outside Europe are excluded. However, even in this case it can be difﬁcult to exclude models that are being jointly used by collaborators both from within and outside Europe and, indeed, modellers may move so the exclusions are not strictly rigorous. Of course some European models have found many applications worldwide. This review is, therefore, complementary to reviews such as Haidvogel & Beckmann (1998) and Grifﬁes et al. (2000) which give a detailed analysis of a limited group of models from around the world.

Glossary, references and web sites As the aim of this review is to illustrate the diversity of models used in Europe, a glossary (Table 1) is included, which is an attempt to rationalise the many acronyms, abbreviations and model names that exist at present. In the glossary, the acronym or model name is explained where possible and a recent reference to its use is given. As further developments often follow a model’s inception, recent references will usually be given rather than original papers to ensure that any recent model improvements are included. It should be noted that sometimes what appears to be an acronym or abbreviation is in fact the name of a person or place. Although traditionally, acronyms or abbreviations are deﬁned at their ﬁrst appearance in a text, in lengthy papers ﬁnding the ﬁrst appearance may take time. The glossary therefore provides a quicker method of ﬁnding deﬁnitions. The glossary also acts as an inventory of named models. In an attempt to give a full inventory, certain models that have no name or acronym, are nevertheless included under the name(s) of the originator(s). This convention applies especially to models that have found wider use. In certain cases there may not be an appropriate reference as the model may be new, or a proprietary product, or is being used in a European project. Also in several European Union projects publications or ﬁnal reports have yet to appear. Therefore web sites are more appropriate and Table 2 is a list of European Union project names and web sites as an adjunct to the glossary. A few abbreviations are assumed. 1-D, 2-D and 3-D naturally mean one, two or threedimensions. EU refers to the European Union, and generally known abbreviations such as UK for United Kingdom are used. Also it is assumed that abbreviations for oceanographic and meteorological institutes are common knowledge although occasionally the nationality may be added for clarity. General geographical knowledge is assumed, Figure 1a–e helps to identify some of the more precise locations mentioned in the text.

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Table 1 ADCIRC AQUAPHY AMAZON ASPECT BB Bi-CGSTAB BIGLOAD2 BLANES BOBA BOM BSHcmod BSHdmod. E

BSHdmod. L

BSHsmod Bryan-Cox-Semtner CANDELA CANIGO CANDIE COHERENS

CONMIN CS3 CSERAM CTCS DAMÉE-NAB

Acronyms, abbreviations and models used within Europe. ADvanced CIRCulation ﬁnite element model of Luettich used by Fortunato et al. (1997). Phytoplankton model Lancelot et al. (1991) coupled with LODYC,Tusseau-Vuillemin et al. (1998). 1-D high resolution ﬁnite volume model for waves in shallow water with overtopping, Hu et al. (2000). Turbulence model, Verdier-Bonnet et al. (1999) see also STRAT-COR. Isopycnal model of Bleck & Boudra (1981, 1986) used by Onken & Sellschopp (1998). Biconjugate stabilised method used in wave propagation model, Oliveira & Anastasiou (1998). Model of stratiﬁed/ baroclinic coastal trapped waves, Brink & Chapman, (1985). Biogeochemical And Nutrient-based Ecosystem Simulator, 1D model, Bahamón et al. (2000a,b). Sea ice model from Swedish Meteorological and Hydrological Institute, Gustaffson et al. (1998). Bergen Ocean Model, Berntsen (2000). Bundesamt fur Seeschiffahrt und Hydrographie circulation model(3-D), Dick & Schönfeld (1996). Bundesamt fur Seeschiffahrt und Hydrographie dispersion model (Eulerian) Dispersion model based on BSHcmod currents. Bundesamt fur Seeschiffahrt und Hydrographie dispersion model (Lagrangian) Dispersion model based on BSHcmod currents. Bundesamt fur Seeschiffahrt und Hydrographie surge model (2-D), Janssen (1996). Model code of Bryan (1969), Semtner (1974) and Cox(1984) see MOM. Analytical model of Candela used by Ducet et al. (1999). EU Project, Canary Islands-Azores-Gibraltar regional development of MOMA model, Johnson & Stevens (2000). Canadian DieCAST, Sheng et al. (1998). Coupled Hydrodynamical-Ecological model for Regional and Shelf Seas, Luyten (1999), disseminated in EU-COHERENS project. Constrained function minimisation scheme, used by Copeland & Bayne (1998). The Met Ofﬁce (UK) Continental Shelf Operational Surge Model, Flather (2000). Sediment and contaminant transport model. Aldridge (1998). Centred-in-time centred-in-space advection scheme, Hecht et al. (1998). Data Assimilation and Model Evaluation Experiment-North Atlantic Basin. Chassignet et al. (2000).

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Table 1 DCSM

DELFT2D-MOR DELFT3D-FLOW DieCAST DIVAST DJM DYMONIS DYMONNS DYNAMO

ECAWOM

ECOHAM1 ECOM

ECOS ECMWF ELISE

ERSEM ESODAE FCT FINEST FLUIDITY FOAM FREISM FUNDY5 FVELLAM

continued

Dutch Continental Shelf Model, surge model prepared by KNMI, DH, Rijkswaterstaat run by KNMI, Gerritsen et al. (1995). 2-D Morphodynamic model, one of the WL-Delft Hydraulics modules used by Nicholson et al. (1997). Core hydrodynamic model of a suite of modules from WL-Delft Hydraulics, Delft Hydraulics (2000). Dietrich-Centre for Air-Sea Technology. Hydrodynamic model of Dietrich et al. (1987). Depth-Integrated Velocities and Solute Transport, 2-D Hydrodynamic model, Lin & Falconer (1997). Abbreviation for the model of Davies (Haidvogel & Beckmann, 1998). Dynamic Model of Nutrients for the Irish Sea, Le Gall et al. (2000). Dynamic Model of Nutrients for the North Sea, Hydes et al. (1996). Dynamics of North Atlantic Models, intercomparison of MOM, MICOM & SPEM, Meincke et al. (2001), DYNAMO Group (1997). Coupled atmospheric(HIRLAM/ HIRHAM), hydrodynamic (PCM-HAMSOM) and wave (WAM) model, Johnson et al. (1999). Ecological North Sea Model Hamburg, Moll (1998). Estuarine, Coastal and Ocean model. Surge forecasting model used by Norwegian Meteorological Institute (DNMI), Flather (2000). Ecological modelling system, Harris et al. (1984). European Centre for Medium-range Weather Forecasting. Environnement Logiciel Interactif de Simulation des Ecosystèmes. Ecological interactive modelling software, Le Pape & Menesguen (1997). European Regional Seas Ecosystem Model, Allen et al. (1998). EU project. North West European Shelf Ocean Data Assimilation and Forecast Experiment. Flux-corrected transport, Hecht et al. (1998). Finland-Estonia 3D ecohydrodynamic model, Tamsalu & Ennet (1995), Ennet et al. (2000). General purpose ﬁnite element CFD code used in oceanic applications (Pain 2000). Forecast Ocean-Atmosphere Model of the Atlantic, The Met Ofﬁce (UK), Bell et al. (2000). Fine Resolution Model of the Eastern Irish Sea, Jones & Davies (1996, 1998). 3D diagnostic model for continental shelf circulation studies, ﬁnite element model. Finite Volume Eulerian-Lagrangian Localised Adjoint Method Model, Healy & Russell (1998).

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Table 1

continued

FVM

Fahrvandsmodellen, operational forecast model for Danish waters based on MIKE3, Jensen et al. (1999).

GBM GHERM

The frontal resolving German Bight model, Dippner (1993). Geo-Hydrodynamics and Environment Research Model, Delhez et al. (1999). Greenland-Iceland-Norwegian Sea. Geophysical Fluid Dynamics model (see MOM), Haidvogel & Beckmann, (1998) use GFDLMas equivalent to MOM. General Ocean Turbulence Model, Burchard et al. (1999). Generalised Minimum Residual, see Bi-CGSTAB. Multi-basin 1-D model of the Baltic, Gustaffson (2000a, 2000b). Generalised Wave Continuity Equation used by Fortunato et al. (1997).

GIN GFDLM GOTM GMRES Gustaffson GWCE H1(Huthnance, Amin, Hall) H2(Huthnance, Amin)

HYCOM

2D cross-shelf circulation model. 2D cross-shelf distribution of suspended particulate matter, Amin & Huthnance (1999). 2D model of stratiﬁed/ baroclinic coastal trapped wave forms, Huthnance (1978). 1D model for continental shelf wave forms, Cartwright et al. (1980). Hamburg Shelf Ocean Model, Backhaus (1985). HAMSOM North Sea version, Pohlmann (1996a). Hydro-Environmental Modelling and Analysis Tool. Unstructured grid, ﬁnite volume model for, water quality and sediment transport, (Falconer, report in preparation). High Resolution Operational Model of the Baltic Sea, Funkquist & Kleine (2000). Hamburg Ocean Primitive Equation model, Février et al. (2000). Harvard Ocean Prediction System, Lermusiaux, (1999). Organic matter microbial degredation model coupled with LODYC, Tusseau-Vuillemin et al. (1998). Hybrid Coordinate Ocean Model. See NERSC-HYCOM.

ISPRAMIX

General circulation model of Eiﬂer & Schrimpf (1992).

JEBAR

Joint Effect of Baroclinicity and Bottom Relief, Sarkisyan & Ivanov (1971), Pierini & Simioli (1998). Multi-layer ocean model, Jensen (1998). Johns et al. (1992) model used by Pinazo et al. (1996). Catalan Continental Shelf High Resolution Model, POM based development by Jordà & Espino, Universitat Politècnica de Catalunya, Spain. Reduced gravity model, Simeonov et al. (1997), Jungclaus et al. (1995).

H3(Huthnance) H4(Huthnance) HAMSOM HANSOM HEMAT

HIROMB HOPE HOPS HSB

JENSEN Johns Jordà & Espino

Jungclaus & Backhaus KPP

K Proﬁle Parameterisation turbulence closure scheme, Large et al. (1994).

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Table 1 LAD_3D Le Provost & Poncet LODYC

MEDMEX MEDMOM MESH3D MICOM MIKE 21 MIKE 3 Minimod MI-POM MI-OIL MOA MOHID2D MOHID3D MOM1, MOM2, MOM3 MOMA MOMOP Monotonic MPDATA MSOU MU-STORM, WAVE, SLICK MUSCL NAUTILUS NERSC-HYCOM NEDWAM NOMADS1/2 NORWECOM NOSTRADAMUS

continued

Lagrangian Advection-Diffusion in 3D devised by Marc Mestres Ridge, Universitat Politècnica de Catalunya, Spain. Finite element model of Le Provost & Poncet (1978), used by Lyard (1997). Laboratoire d’Oceanographie Dynamique et de Climatologie model, Madec et al. (1991), Tusseau-Vuillemin et al. (1998). See OPA. EU Project, Mediterranean models evaluation experiment. Mediterranean model based on GFDL-MOM, Roussenov et al. (1995). Finite volume version of MOHID3D, Martins et al. (2001). Miami Isopycnic Coordinate Ocean Model, Vigan et al. (2000). Also known as System 21, Bach et al. (1997). 3-D non-hydrostatic general model also known as SYSTEM3 being used in FVM. Limiter function in TVD advective scheme, James (1996). Norwegian Meteorological Institute (DNMI) version of POM, Engedahl (1995). Operational oil-spreading model, accepting data from MI-POM, and WAM, Martinsen & Melsom (1994). Open boundary condition, Palma & Matano (2000). Modelo Hidrodinâmico, 2D hydrodynamic model Taboada et al. (1998). 3D double sigma version of MOHID2D developed by Santos (1995) see MESH3D. Geophysical Fluid Dynamics Modular Ocean Model (various versions), Johnson & Stevens (2000). Modular Ocean Model, Array processing form, Myers et al. (1998), Johnson & Stevens (2000). Met. Ocean Modelling Project, Røed et al. (1995). Limiter function in TVD advective scheme, James (1996). Multidimensional Positive Deﬁnite Advection Transport Algorithm, Hecht et al. (1998) from Smolarkiewicz (1984). Monotonic Second-Order Upwind scheme, Vested et al. (1996). Several of the models developed at MUMM. Monotonic Upstream Schemes for Conservation Laws-, Limiter function in TVD advection scheme, James (1996). 3-D quadrilateral ﬁnite element model, Espino et al. (1998). Nansen Environmental and Remote Sensing Centre version of HYCOM, Evenson & van Leeuwen (2000). Implementation of WAM in the Netherlands, Flather (2000). North Sea Advection-Dispersion Models, EU projects. Tartinville et al. (1998). Norwegian Ecological Model System, Skogen (1993). 2D advection-diffusion model for sediments and water quality in the Southern North Sea, Tappin et al. (1997b).

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Table 1 Noye NUBBLE

NWECS NZB OCCAM OCKE3D OHAD OPA OPYC OPCOM OSMOM PARTIC PCFLOW3D PCM-HAMSOM Pel-GKdV Per-EISM PHYTO_1D POL3DB POLCOMS POL2G POM POSEIDON PPM PRISM PROBE PROBE-Baltic

continued

Model by B.J. Noye used in Wash, UK. Young et al. (1998) use output from the model. A turbulent boundary layer model for the linearized shallow water equations used with 1-D adaptive grid by Fortunato & Oliveira (1999). North-West European Continental Shelf. NoordZee Basis model (3D baroclinic model for Southern North Sea nested in DCSM). Gerritsen et al. (2000). Ocean Circulation and Climate Advanced Modelling Project, Webb et al. (1997). 3D Finite volume coastal ﬂow model, Arnoux-Chiavassa et al. (1999). Hadley Centre Ocean Model, Février et al. (2000). Hydrodynamic model developed at LODYC, Février et al. (2000), Roullet & Madec (2000). Isopycnal general circulation ocean model, Kauker & Oberhuber (1998), Holland et al. (1996). EU Project. Operational Modelling for Coastal zone mangement. Oslo Multilayer and Mesoscale Ocean Model, Røed & Shi (1999). Particle tracking module used with MOHID2D, GomezGesteira et al. (1999). 3-D hydrodynamic model Cetina et al. (2000). Programa de Clima Marítimo version of HAMSOM, used in ECAWOM project. Pelinovsky, Generalised Korteveg-de Vries model, Holloway et al. (1999). Periáñez Eastern Irish Sea Model, Periáñez (1999). 1-D couple turbulence closure and primary production model, Sharples (1999). Proudman Oceanographic Laboratory 3-Dimensional B grid model (see POLCOMS), Proctor & James (1996). Proudman Oceanographic Laboratory Coastal-Ocean Modelling System. Holt & James (2001). Proudman Oceanographic Laboratory, 2-dimensional general purpose model, Jones (1999). Princeton Ocean Model, Blumberg & Mellor (1987), Mooers & Wang (1998), McClimans et al. (2000). Nested POM based operation scheme for the Aegean, Soukissian et al. (1999). Piecewise Parabolic Method advection scheme. James (1996, 2000). Modiﬁed version of DJM used in Irish Sea by Aldridge (pers. comm.). Program for Boundary Layers in the Environment, Svensson (1998). Use of PROBE in the Baltic, Omstedt & Nyberg (1996).

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Table 1 PROSPER (PGCM) PRO-WAM QUICK

QUICKEST

QUODDY RCO RESTWAQ

RGPM ROMS SAM-2DH/SAM-3D Sarkisyan (diagnostic) Sarkisyan (prognostic) SCAWVEX SCOBI SCRUM SEA SEASCAPE SEDBIOL SEOM SLOSH SNSEA SOMS SPbGNOME SPEM SPUDS Stanev & Roussenov Stommel STRAT-COR SUPERBEE SWAN

continued

PROSPER General Circulation Model based on SOMS, Zuur (1991). WAM model used in PROMISE EU project. Quadratic Upstream Interpolation for Conservative Kinematics, third-order upwind advective scheme, Leonard (1979), Hecht et al. (1998). Quadratic Upstream Interpolation for Conservative Kinematics with Estimated Streaming Terms, Vested et al. (1996), Hecht et al. (1998). Finite element model, Lynch & Werner (1991). Rossby Centre regional ocean climate model, a further development of OCCAM, Meier (1999). Remote sensing as a tool for improved knowledge for water quality and ecology. Linking of remote sensing images to water quality models. Reduced gravity model, Jungclaus et al. (1995). Regional Ocean Modeling System, Haidvogel et al. (2000). 2-D and 3-D hydrodynamic models, Brenon & Le Hir (1999). 3-D model, Trukhchev & Stanev (1983). 3-D model, Stanev (1980). EU Project, Surface Current and Wave Variability Experiments. Swedish coastal zone biogeochemical model coupled to PROBE, Swedish Meteorological and Hydrological Institute. S-coordinate Rutgers University Model, Haidvogel et al. (2000). Southampton-East Anglia parallel ocean circulation model a development of OCCAM. Pollution dispersal module for NAUTILUS. 1-D depth resolving model coupling physics/microbiology/ sediments, Smith & Tett (2000). Spectral Element Ocean Model (Finite element). Haidvogel & Beckmann (1998). Model used in Shi et al. (1997) for storm surge inundation. POL3DB, 2.4 km model of southern North Sea, Proctor & James (1996). Sandia Ocean Modelling System, Zuur & Dietrich (1990). St. Petersburg Group of Numerical Ocean Modeling and Exploration. Model used in Androsov et al. (1998a, 2000). S-coordinate Primitive Equation Model, Kliem & Pietrzak (1999), Heniche et al. (2000). Precurser of QUICK, Leonard (1995). 2D Shallow water model, Stanev & Roussenov (1983). Thermohaline convection model, Stommel (1961). Turbulence model (stratiﬁed-Coriolis), Verdier-Bonnet et al. (1999) see also ASPECT. Limiter function for TVD advective scheme, James (1996). Simulating Waves Nearshore, wave model, Booij et al. (1999).

48

COASTAL AND SHELF-SEA MODELLING

Table 1 SYKON

SYMPHONIE SYSTEM-21 SYSTEM-3

continued

Synthesis and New Concept of North Sea Research, German Federal Ministry of Education and Research (BMBF). Moll & Radach (2001). Adaptation of Johns et al. (1992) used by Estournel et al. (1997) and Marsaleix et al. (1998). See MIKE21. 3-D non-hydrostatic model, Vested et al. (1998), see MIKE3.

TELEMAC 2D-(SUBIEF. . . . etc.) 2-D ﬁnite element model developed by Electricité de France, hydrodynamics + modules for sediment and other processes. TELEMAC-3D 3D ﬁnite element model developed by Electricité de France, Jankowski et al. (1996). TRIM2D Hydrodynamic model Schneggenburger et al. (2000), Rosenthal et al. (1998), Malabib et al. (2000). TRIM3D Hydrodynamic model Casulli & Stelling (1998). TRIMPP Massively parallel version of TRIM3D developed by Kapitza & Eppel, GKSS research centre, Geesthacht, Germany. TRISULA Hydrodynamic model developed at WL-Delft Hydraulics, Bijvelds et al. (1999). TRIVAST 3-D version of DIVAST. TRIWAQ 3-D ﬁnite difference hydrodynamic model used at RIKZ, Netherlands. TVD Total Variation Diminishing advection scheme, James (1996). ULTIMATE ULTIMATE-QUICKEST UPWIND

Universal Limiter for Transient Interpolation Modelling of the Advective Transport Equations, Vested et al. (1996). Monotonic advection scheme, Vested et al. (1996). Explicit advection scheme, Vested et al. (1996).

VICTOR

Vertically Integrated Computations of Tides For Oceanographic Research, George & Stripling (1995).

WAM Wang

Wave model, Monbaliu et al. (2000). Wang (1982, 1989) model used by Tintoré et al. (1995), Ardhuin et al. (1999). The model developed by WL-Delft Hydraulics and the Rijkswaterstaat which was the basis of the DCSM. Wave model, Albiach et al. (2000). West Coast Model of the UK, Davies & Jones (1992).

WAQUA WAVEWATCH-II WCM

A review of reviews As the topic of shelf-sea modelling has developed, reviews of different aspects of models and model components as well as inventories have occasionally appeared. Before looking into the details of model technology in the present review, for convenience it is worth listing some recent papers and reports which have, in their own way, given an overview of models or model components. Table 3 lists a number of such papers, reviews or reports with a brief comment as to their contents. 49

50

PROFILE PROMISE PROVESS SCAWVEX SEANET-DI SEDMOC SIMIP SOFT STOWASUS TOPAZ

DYNAMO DYNOCS ESODAE EUROROSE FANS GANES INDIA MAIA MAXWAVE MEDMEX MFSPP NOMADS NOMADS2 OPCOM PIONEER

DIADEM

Huthnance 1997a,b http://www.pol.ac.uk/promise/ http://www.nbi.ac.uk/provess/ http://www.shef.ac.uk/∼sceos/environmental/scawvex/home.html http://www.minvenw.nl/rws/projects/seanet/ http://hydr.ct.tudelft.nl/wbk/public/sedmoc http://www.ifm.uni-kiel.de/me/research/Projekte/SIMIP/summary.html Web site not available as yet http://web.dmi.dk/pub/STOWASUS-2100/ http://fram.nrsc.no/∼geir/hycomstuff.html

http://www.ifm.uni-kiel.de/to/dynamo/dyn_m.html http://www.dhi.dk/dhiproj/country/europe/dynocs http://www.metofﬁce.gov.uk /sec5/ ESODAE/ ESOHOME.html http://ifmaxp1.ifm.uni-hamburg.de/EuroROSE/ http://www.upc.es/lim /eng/pro/fans/ http://www.met.ed.ac.uk/ganes/ http://www.pol.ac.uk/india/ INDIA.html http://www.sintef.no/units/civil/coastal/ Maia/index.html http://w3g.gkss.de/projects/maxwave/ http://modb.oce.ulg.ac.be/ MEDMEX http://www.cineca.it /∼mfspp000/ http://www.pol.ac.uk /coin /nomads/ http://www.pol.ac.uk /coin /nomads2/ http://www.hydromod.de/projects/OPCOM/OPCOM.html http://pioneer.geogr.ku.dk/main.html

http://www.theyr.is/diadem /

http://www.marine.ie/datacentre/projects/canigo http://www.ifm.unihamburg.de/∼wwwto/ResearchTopics/CARTUM/ carthome.htm http://www.climerod.com/ http://alpha2.mumm.ac.be/∼patrick/mast/

Canary Islands-Azores-Gibraltar Observations. Comparative Analysis and Rationalization of Second-Moment Turbulence Models. Inﬂuence of climate change on coastal sediment erosion. Dissemination and exploitation of a COupled Hydrodynamical Ecological model for REgioNal Shelf seas Development of advanced data assimilation systems for operational monitoring and forecasting of the North Atlantic and Nordic Seas. Dynamics of North Atlantic Models Dynamics of Connecting Seas European Shelf Seas Ocean Data Assimilation and Forecast Experiment European radar ocean sensing Fluxes Across Narrow Shelves. Global AssimilatioN applied to modelling of European Shelf seas Inet Dynamics Initiative: Algarve. Monitoring the Atlantic Inﬂow toward the Arctic Rogue waves – Forecast and impact on marine structures Mediterranean models evaluation experiment. Mediterranean Forecasting System Pilot Project. North Sea Model Advection Dispersion Study North Sea Model Advection Dispersion Study – 2 Operational Modelling for Coastal zone Management Preparation and integration of analysis tools towards operational forecast of nutrients in estuaries of European rivers Processes in Regions of Freshwater Inﬂuence Pre-operational Modelling in the Seas of Europe. Processes of Vertical Exchange in Shelf Seas. Surface Current And Wave Variability Experiment. SeaNET Data Interface Sediment Transport Modelling in Marine Coastal Environments Sea-Ice Model Intercomparison Project Satellite-Based Ocean Forecasting Regional STOrm, WAve and SUrge Scenarios for the 2100 century Towards an Operational Prediction system for the north Atlantic and European coastal Zones

CANIGO CARTUM

CLIMEROD COHERENS

Web page or reference

Title

EU projects and projects involving European partners.

Acronym

Table 2

J. E. JONES

COASTAL AND SHELF- SE A MO D E L L I N G

Table 3

A selection of review papers relevant to coastal and shelf sea modelling in Europe.

Review topic

Reference

Boussinesq type models Small scale processes in 2-D, 3-D SPM and waves. Ten methods of wetting and drying. Five advection methods. Storm surge modelling. Five models used in the DAMEE-NAB experiment. Models presented at the JONSMOD 1996 conference. Review of tidal spectral models. Review of turbulence closure tidal models. Four sediment transport models. Comparison of models of the Greenland-Iceland-Scotland ridge. A comparison of the OHAD, OPA and HOPE models. Existing European operational modelling. Compares discretisation and time-stepping of 19 ocean models. Reviews 15 European, US and Australian models. Review of tracer advection schemes. Reviews aspects of advection schemes. Review of the impact of shelf waves on a tidal model. A review of turbulence closure schemes. A book of papers on model skills and accuracy. A review of ﬁnite-element models. The MIKE3 and POM models compared with reference to pressure gradients. A summary of the POM model. Review of 3-D ecological (including hydrodynamic) models of the North Sea. Wave modelling in the PROMISE project. Five different models used on an off-shore breakwater test. PROMISE literature review on turbulence, waves and suspended matter transport. Seven models which simulate waves and currents in the surf zone. Ten cross-shore sediment models. A comparison of three European models. A comparison of the idealised models used in the NOMADS project.

Agnon et al. (1999) Baumert et al. (2000) Bolzano (1998) Bell (1998) Bode & Hardy (1997) Chassignet et al. (2000) Dyke & Davies (1998) Davies et al. (1997a) Davies et al. (1997b) Davies A. G. et al. (1997) DYNAMO Group (1997) Février et al. (2000) Flather (2000) Grifﬁes et al. (2000) Haidvogel & Beckmann (1998) Hecht et al. (1998) James (1996) Lam (1999) Luyten et al. (1996) Lynch & Davies (1995) Lynch et al. (1996) McClimans et al. (2000) Mooers & Wang (1998) Moll & Radach (2001) Monbaliu et al. (1999) Nicholson et al. (1997) Nöhren et al. (1997) Péchon et al. (1997) Schoonees & Theron (1995) Smith et al. (1996) Tartinville et al. (1998)

General models From the questionnaire responses most models were regarded as generally applicable, very few were speciﬁc to a certain area. Of course some examples like the Rossby Centre regional ocean climate model (RCO, Meier 1999) have been developed, in this case from the OCCAM model, with speciﬁc application to the Baltic in mind, but it is a general model which could be used elsewhere. Notable exceptions are the advection-diffusion models NOSTRADAMUS, DYMONNS and DYMONIS as they rely on input hydrodynamic ﬂows from other models and these will have been generated for a particular area. Nevertheless, 51

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even the basis of these models could be applied elsewhere. There did not seem to be any model speciﬁc to a single application only.

New model or old? It was interesting to see that only about 10% of the models reported were completely new. Most of them were new uses of existing models or developments of them. A small number of model names kept recurring because they have been widely used in Europe. These models are all included in the glossary and their existence as the core of various modelling systems or operational schemes (see section on “Operational Oceanography”, p. 105) indicates that their basic hydrodynamics are regarded as sound.

The hydrodynamics The starting point – the equations Simpler formulations At the most basic level, a model is an equation, or set of equations, that describes a desired quantity (e.g. current, surface elevation, temperature or other parameters) in terms of other known quantities. Simple formulations may be developed by researchers themselves or borrowed for speciﬁc situations. For example, Ryrie (1995) used a simple formulation looking at tidal power generation assuming given external hydrodynamics. Pavelson et al. (1997) used a semi-analytic model relating changes in time and space of stratiﬁcation to different forcings in the Gulf of Finland. Aelbrecht et al. (1999) used a simple analytical model in conjunction with observations and experiments to show how Lagrangian transport along a coast can result from oscillatory tidal forcing that causes an oscillating Ekman layer. In an idealised case they show that interaction with the wall representing the coast causes a coastal residual current similar to that seen in the English Channel. Guizen et al. (1999) demonstrated an analytical model of the behaviour of an incident barotropic Poincaré wave as it approaches step geometry representative of the shelf break. They showed that the angle of incidence is important and that the model compares well with observations if a two-layer assumption is fulﬁlled. On a larger scale Ducet et al. (1999) used the Candela (1991) analytical model to show that Black Sea elevation was not an inverse barometer, possibly due to the narrowness of the Strait of Bosporus. Another large-scale analytic model was demonstrated by Pichevin (1998) who surveyed the parameter space governing baroclinic instabilities in three layer ﬂow. The two most signiﬁcant parameters were identiﬁed and the very important role of bottom slope was noted.

Hydraulic theory Formulations may be borrowed from hydraulic theory. Mattson (1995) related ﬂow through the Öresund to sea-level differences but because of the rotational effects a modiﬁcation to the standard formulae was suggested. Lafuente et al. (2000) used time dependent hydraulic 52

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theory with a composite Froude number for two or more layers to examine tides in the Strait of Gibraltar. Another hydraulic model was used by Laanearu et al. (2000) to examine the ﬂow of the Strait of Irbe (Gulf of Riga-Baltic). Tsimplis et al. (1999) used hydraulic theory to examine the overﬂow of Cretan deep water into the Eastern Mediterranean.

Boussinesq equations In other cases certain standard equations, particularly wave equations, can act as the starting point for describing a system. Some researchers use the Boussinesq equation (not to be confused with the Boussinesq approximation – see p. 54) which describes weakly nonlinear weakly dispersive waves. For example, studying waves in the presence of currents, Chawla & Kirby (2000) start from the Boussinesq equation. Rakha (1998) used a Boussinesq wave module in a quasi 3-D model in the context of hydrodynamics leading to sediment transport. Ozanne et al. (2000) compared the performance of a 1-D Boussinesq model with a laboratory experiment to examine the possibility of predicting surf zone velocity moments. Brandt et al. (1996) used a Boussinesq model to look at internal waves in the Strait of Gibraltar. Schäffer & Madsen (1995) took the classic Boussinesq equation and suggested various improvements. Indeed, Agnon et al. (1999) in a review of the topic of Boussinesq models commented that such models have become an attractive tool for general coastal applications after the introduction of the Padé approximations. In Newberger & Allen (1996) a comparison is made between a Boussinesq equation model, a reduced system model, and a model based on primitive equations. The reduced system model is itself a form of the Boussinesq equation with modiﬁcation of the non-hydrostatic terms.

Korteweg-de Vries equations Other models employ the Korteweg-de Vries equations used for describing non-linear long waves. An extended form of these equations has been used to investigate internal waves in the Black Sea by Ivanov et al. (1994), in the Baltic by Talipova et al. (1998) and to look at internal bores at the Malin Shelf by Pelinovsky et al. (1999). Further use of the extended Korteweg-de Vries equations to examine internal waves, initially in an idealised situation and then at the Crimean coast, is reported in Pelinovsky et al. (1994) and off the coast of Israel (Pelinovsky et al. 1995). More theoretical aspects of the Korteweg-de Vries equation and its extensions may be found in Grimshaw et al. (1997) and Holloway et al. (1998). Further applications to examine internal tide transformation on a coastal shelf are given in Holloway et al. (1997, 1999).

Navier-Stokes, Saint-Venant equations, primitive equations Perhaps the most general starting point for the hydrodynamical modelling of shelf seas has been a set of equations usually known as the Navier-Stokes equations. It is of interest to note that there is a matter of historical debate (Anderson 1998) whether they should be termed Saint-Venant equations. Apparently a set of equations equivalent to the Navier-Stokes set were published earlier by Saint-Venant. However, these 3-dimensional sets of equations contain terms such as vertical accelerations which are not required in all cases and so a derived set of equations, known as “primitive equations” (Zuur 1991) are traditionally used 53

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as the starting point for most of the hydrodynamic ocean models discussed in this review. The primitive equations usually consist of a set of momentum and continuity equations with perhaps additional terms for temperature and salinity (Beckers 1991, 1992).

Other methods It must be mentioned that there are alternative methods of modelling shelf seas that do not rely on such formulations. Greve & Lange (1999) used a neural network to predict plankton in the North Sea and found that by comparison with observations this gave the best representation, followed next by a statistical method and then a numerical simulation model. Röske (1997) also used neural networks for sea-level forecasting and compared them with six other models, two hydrodynamic, one statistical, one nearest neighbour, the persistence model and, ﬁnally, expert forecasts based on model output. Mention must also be made of conceptual models that examine and help to identify the underlying processes in various systems without actually carrying out a quantitative numerical calculation. Skreslet (1997) considered the freshwater discharge of Norwegian meltwater and how it would propagate into the Arctic Ocean (referred to as the Arctic Mediterranean) through various food webs. Another conceptual model is that of Czitrom & Simpson (1998) who consider land runoff and its inﬂuence on frontogenesis in Liverpool Bay.

The approximations By tradition several approximations are used in many numerical models, the most common being the Boussinesq and the hydrostatic.

Boussinesq The use of the term “Boussinesq model” can sometimes confuse. As seen above the Boussinesq equation can be used for a model. The Boussinesq approximation used in the great majority of shelf models states that while it is recognised that water can change its density, if such ﬂuctations are small their effect on the mass of the ﬂuid can be neglected but their effect on the weight must be retained. Thus, an average density may be used in the momentum equations but buoyancy effects must be included. This is a very common assumption; the responses to the questionnaire showed that all models where this approximation could apply did in fact use it. An evaluation of its use is given in Mellor & Ezer (1995).

Hydrostatic The hydrostatic approximation simply ignores those terms in the basic Navier-Stokes equations that deal with vertical accelerations. It is assumed that the only vertical movements are those due to density or which arise from continuity considerations. From the questionnaire and literature survey it was apparent that most modellers use this approximation. McClimans et al. (2000) compared the MIKE 3 non-hydrostatic model with POM in the Skaggerak. Vested et al. (1998) described a general non-hydrostatic model SYSTEM3, which is in fact almost identical to MIKE 3, which proceeds directly from the Navier-Stokes equations to examine the rate of dense water formation in the Adriatic using surface conditions 54

COASTAL AND SHELF- SE A MO D E L L I N G

from climatology or from the ECMWF. Unfortunately, workers have reported that nonhydrostatic schemes can be computationally expensive but Marshall et al. (1998) used an algorithm to implement a non-hydrostatic scheme only in locations where it is necessary. Rasmussen et al. (1999) reported the use of a non-hydrostatic primitive equation model of the Greenland–Iceland Norwegian Sea (GIN) which is coupled to a dynamic thermodynamic sea-ice model. Very often the hydrostatic approximation is invoked without further explanation. Tintoré et al. (1995) did, however, take the trouble to justify its use, in this particular instance because they were dealing with long-period waves. As mentioned in the previous section, the Boussinesq-type model of Brandt et al. (1996) is non-hydrostatic, even if in a weakly non-hydrostatic form. Kämpf & Backhaus (1998) had to use a non-hydrostatic model in an Arctic situation due to the presence of strong convection from brine driven currents. Casulli & Stelling (1998) gave a good overview of the topic by demonstrating the effect of the hydrostatic approximation in some idealised cases. It is interesting to note that they emphasise the use of non-hydrostatic code in engineering applications. Recently Tsarev (2001) reported the use of a non-hydrostatic model examining the propagation of nearbottom water in the Baltic. An interesting aspect of this report was a comment about the effect of model resolution in representing non-hydrostatic effects.

Describing the domain, dividing the domain Co-ordinate systems and discretisation The equations described above are generally formulated in terms of a co-ordinate system in which the domain of interest may be described. In present modelling usage there are several possible co-ordinate systems used in the horizontal and vertical and these are described below. In certain simple applications a completely analytical solution over the whole domain of interest is possible so that whatever co-ordinate system is used it is possible to obtain the desired parameters at any required location in space and/or time. However, in complex systems where only a numerical solution is possible some form of discretisation is required. The selection of a co-ordinate system and the subsequent discretisation if required, are highly important steps, because they inﬂuence the way parameters are represented, the methods of solution, and which processes can be resolved. For example, Barnier et al. (1998) made the comment that only recently has there been interest in investigating the effect of co-ordinate systems on model results. From the point of view of stability Beckers (1994) considered some of the stability criteria required for discretised n-dimensional advection–diffusion equations. It is worth noting here that it is useful, where possible, to compare analytic or semi-analytic models with numerical models as a check and to provide possible insight. Luyten (1996) used both an analytical and a numerical scheme to look at the evolution of a tidally-generated bottom boundary layer and a wind-generated surface layer in view of application to thermal stratiﬁcation in shelf seas. Fennel & Mutzke (1997) used the GFDL model with a free surface in an idealised case and compared it with an analytical model. For certain purposes even conventional spatial co-ordinate systems need not be used. For example, de Szoeke (2000) expressed the equations of motion in thermodynamic co-ordinates, Pers & Rahm (2000) used salinity as the space variable and Molls & Molls (1998) and Zoppou & Roberts (1999) suggested combining space and time as coordinates on the same footing. 55

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Horizontal co-ordinate systems In the horizontal, the natural co-ordinate system on the Earth is the χ, ϕ (spherical) set which are simply orientated along lines of longitude and latitude and are locally orthogonal. From the responses to the questionnaire, spherical co-ordinates were used by the great majority of models. For example, almost all of the national operational surge forecasting models listed in Flather (2000) use spherical co-ordinates. However, for modelling purposes problems can arise with this system in polar regions due to the increasingly narrow spacing of the χ (longitude) co-ordinates (see CFL criterion later). Transformations of this natural set of coordinates are possible. In a localised area a ﬂat surface may be assumed and simple Cartesian co-ordinates used in the horizontal. Flather (2000) reported that the ECOM surge prediction model run by the Norwegian Meteorological Institute is run on a Cartesian grid based on a polar stereographic projection. Another transformation may be to orthogonal curvilinear co-ordinates to ﬁt particular coastal conﬁgurations (see p. 60). Finally, co-ordinates in the horizontal may themselves be speciﬁed in terms of functions. All these types of horizontal co-ordinate have been used in European models.

Horizontal discretisation In those models that require it, discretisation in the horizontal has been traditionally in terms of ﬁnite differences, ﬁnite elements or ﬁnite volumes. There are other possible, less common, methods of discretisation. Mention must be made of collocation techniques where highly accurate solutions are sought at speciﬁc points, the solution at other points being derived by interpolation (Ehrenstein & Peyret 1989). A distinctly different approach was reported in Du (1999) where several different forms of the ﬁnite-point method are reviewed. These potentially offer meshless methods where the solution is deﬁned at an arbitrary ﬁnite number of points. A further method that should be mentioned is the boundary element scheme for the solution of ﬂuid dynamic problems. This method relies on deriving solutions along the boundaries of the domain of interest ﬁrst and then in the interior if required. Although this scheme, may have uses in particular situations where the area of interest is close to the boundaries, it is generally more difﬁcult to implement. Boundary element and ﬁnite difference techniques are compared in Arnold & Noye (1985). In a tidal model of the Siberian shelf Androsov et al. (1998a) discussed a case which may be considered to be two boundary value problems, one large scale and one local scale, and they are solved jointly. In this review, therefore, apart from some analytic and semi-analytic cases the main models considered will be ﬁnite difference, ﬁnite element and ﬁnite volume schemes. It is interesting that both from the literature search and the questionnaire, ﬁnite difference schemes are by far the most common form used in coastal modelling at the moment.

Finite element methods A desirable property of the ﬁnite element scheme, which has its origins in engineering problems, is that it usually employs an unstructured grid and is therefore of use in areas with highly complicated geometries. In the European context some ﬁnite element models have been developed and applied by European researchers. However, ﬁnite element models, 56

COASTAL AND SHELF- SE A MO D E L L I N G

km 4540

– 10 0 –2 0 –3 0 00

Cape Salou

–50

00

–4

–6

00

4520 Ebro Delta Ebro river

4500

4480

–5

0

4460

00

4440

–1

4420

280

300

000 –340 0 – 0

–2

320

340

360

km

Figure 2 Quadrilateral ﬁnite element grid of the Catalan Shelf, northeast Spain. By courtesy of M. A. Maidana & M. Espino.

especially from the United States, have also been employed in European scenarios. Models of European origin include the TELEMAC system originally developed by Electricité de France. Lyard (1997) demonstrated a tidal ﬁnite element model of the Arctic Ocean, which is a development of the model of Le Provost et al. (1995). Zecchetto et al. (1997) used a 2dimensional ﬁnite element model developed by Umgiesser (1986) and presented by Umgiesser & Bergamasco (1993), to simulate storm surges in the Lagoon of Venice and Iakovlev (1998) described a baroclinic ﬁnite element model of the Arctic Ocean. Finite element model grids usually adopt triangular elements but the quasi 3-dimensional NAUTILUS model reported by Espino et al. (1998) and originally developed by Espino (1994) uses a quadrilateral element (Fig. 2). The model is used to investigate the mechanisms that are responsible for the circulation on the Catalan shelf. Finite element grids are not necessarily ﬁxed for all time. The general purpose computational ﬂuid dynamics code FLUIDITY ( Pain 2000), which has oceanic applications, uses a moving ﬁnite element/spectral element method 57

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that allows arbitrary movement of the mesh with time, adapting the grid where necessary for increased resolution. Barros (1996) examined the Tejo estuary, Portugal using a ﬁnite element model derived from the TEA-NL model of Westerink et al. (1988) as background hydrodynamics to a study evaluating parameterisations used in sediment modelling. For the same estuary, Fortunato et al. (1997) used the ADCIRC (ADvanced CIRCulation) ﬁnite element model (Luettich et al. 1991, Westerink et al. 1992) to examine tidal currents. The start of the paper by Lynch et al. (1996) gives a useful review of ﬁnite element modelling. A further European application of a model that originated in the Unites States is the spectral ﬁnite element ocean model SEOM used by Curchitser et al. (1999) to simulate the abyssal ﬂow in the eastern Mediterranean. In the paper they give details of the h-p ﬁnite element method, which is also examined in Barragy & Walters (1998). Here h governs the grid element resolution and p governs the resolution and interpolation within each element. In this model there are 656 elements and within each quadrilateral element there is a 7 × 7 and a 5 × 5 spectral expansion for velocity and pressure points, respectively. Note that in hydrodynamical use generally the ﬁnite element gridding bears some relation to the bathymetry, the coarser mesh elements being usually in the deeper water, the ﬁner in shallower water. This gridding reﬂects the fact that in deeper water, tides and other barotropic disturbances move at greater speed. The mesh size is therefore dictated by stability considerations (see CFL criterion, p. 68). Finite elements can, however, be used in other ways. Brasseur et al. (1996) used an inverse variational ﬁnite element method to grid historical observations to produce seasonal temperature and salinity ﬁelds for the Mediterranean. In a subsequent paper Denis-Karaﬁstan et al. (1998) perform the same task for seasonal variation of nitrates. For such schemes the gridding may have a different emphasis and may not be related to bathymetry. Vigan et al. (2000) also used an inverse variational ﬁnite element model to test the possibility of estimating sea surface velocity from satellites by observing spatial patterns of sea surface temperature. As test input they actually used temperature ﬁelds from an existing ﬁnite difference model (MICOM). An unstructured grid is not solely within the province of the ﬁnite element method. The recently developed HEMAT-2DH model (R. Falconer, pers. comm.) uses an unstructured triangular mesh but it is solved using the ﬁnite volume method. Similarly the TELEMAC2D system offers a ﬁnite volume option.

Finite volume The most useful property of the ﬁnite volume method, which is based on the calculation of ﬂux divergences, is that it is strictly conservative. Stansby & Lloyd (2001) commented that in ﬁnite difference and ﬁnite element schemes spatial discretisation can be made of arbitrarily higher order whereas ﬁnite volume is generally limited to second-order accuracy. Finite volume methods, however, guarantee global conservation. Examples of the use of ﬁnite volume methods are Mingham & Causon (1998) who used a high resolution time marching method for solving 2-D shallow water equations. The model of aetina et al. (2000) that is concerned with the conservation of radiological material naturally uses a ﬁnite volume scheme to ensure conservation. Similarly, the AMAZON model reported by Hu et al. (2000) uses a 1-D high resolution ﬁnite volume scheme to simulate waves in shallow water with the overtopping of sea walls. The OCKE3D coastal ﬂow model uses ﬁnite volumes to calculate 58

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salinity and total suspended matter budgets in the presence of fronts and river plumes (Durand et al. in press). Another recent ﬁnite volume model consists of 11 coupled 1-D basins covering the Baltic Sea, part of which is described in Gustaffson (2000a,b) in which volume exchange between sub-basins is important from the point of a sea-ice model component (Bjork 1992, 1997) and a biochemical sub-model. In certain instances the ﬁnite volume scheme may be applied in the vertical only; the TRIVAST model (Lin & Falconer 1997) uses ﬁnite volumes in the vertical but ﬁnite differences in the horizontal. The NZB model of the southern North Sea, which is nested (see nesting p. 60) in the Dutch Continental Shelf Model (DCSM) (Gerritsen et al. 2000, Vos et al. 2000) used ﬁnite volumes for the transport of conservative matter but used ﬁnite differences for the momentum and continuity equations. Martins et al. (2001) used a 3-D primitive equation model based on the ﬁnite volume approach but with a new concept for vertical discretisation (see p. 65). The PROBE-Baltic 13 sub-basin model reported in Omstedt & Nyberg (1996), Omstedt & Axell (1998) and Omstedt & Rutgersson (2000) incorporates a ﬁnite-volume type approach to ensure volume conservation of water in the presence of freezing. Finite volume methods also have other applications. In Adcroft et al. (1997) a ﬁnite volume approach (shaved cell) is used to contour the bottom cell of z-level models (see p. 62) to more closely conform to the actual bottom topography (Grifﬁes et al. 2000). Wu & Falconer (2000) introduced within a ﬁnite difference model a ﬁnite volume control scheme to ensure mass conservation in simulating salinity and cohesive sediment transport in estuaries.

Finite difference From the responses to the questionnaire and from the literature search it was clear that by far the most popular method of horizontal, and indeed vertical, discretisation was the ﬁnite difference method. From the questionnaire, approximately 90% of respondents used a ﬁnite difference scheme. As this is apparently such a popular scheme at present, in the rest of this review all models will be assumed to be of the ﬁnite difference type in both the horizontal and vertical unless speciﬁcally stated. In a regular orthogonal ﬁnite difference grid, by tradition the points at which the discretised elevations, east- and north-directed currents and scalars are calculated, may be in a staggered form. The most common of these is the series of grid patterns given in Mesinger & Arakawa (1976) labelled A, B, C, D, etc. The questionnaire tried to determine the type of staggered grid used by modellers and by far the most popular scheme was the “C” grid used by about 85% of ﬁnite difference modellers. The popularity of this scheme is also noted in a recent review (Arakawa 2000). The Arakawa “B” was the next most popular. In certain cases the use of the B grid was a natural consequence of the use of the GFDL/MOM code. In fact the B grid tends to be used in ocean models rather than in the coastal zone (Grifﬁes et al. 2000). It is also subject to a “checkerboard” grid scale problem when propagating tidal motion in shallow water. However in the POL3DB model the B grid is used for the speciﬁc reason of ensuring the sharpness of fronts when used with the PPM advection scheme (see p. 71). The use of a ﬁlter, Killworth et al. (1991), ensures a stable propagation of tides (Jones et al. 1998). In the responses there were some notable exceptions to the dominance of the B and C grids. Sheng et al. (1998) explained how the DieCAST model uses a co-located C and A grid, whereas the CANDIE version of this model reverts to the C grid. In the paper they used a standard test by Haidvogel & Beckmann (1998) to compare it with the MOM model, which is on a B grid. The Hamburg Ocean Primitive Equation (HOPE) model was originally 59

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formulated on an “E” grid (Février et al. 2000) but a new version based on the “C” grid is under development. One disadvantage of a regular ﬁnite difference scheme is that unlike unstructured ﬁnite elements it cannot simultaneously provide ﬁne resolution in areas of interest and coarse resolution elsewhere for economical computation. Some ﬁnite difference schemes have evolved in response to this problem.

Nesting Nesting one rectangular model mesh inside another is a way of obtaining ﬁne resolution in areas of interest. This nesting can be dynamically connected (two-way) or the inner model simply uses information from the outer model as its open boundary prescription (one-way). Care has to be taken along the interface which is effectively the open boundary of the inner model or models (see p. 87). An example of a simple one-way nesting is the use of the 7-km resolution WCM model of the West Coast of the United Kingdom to provide far ﬁeld surge information for the 1-km resolution FREISM model of the Eastern Irish Sea (Jones & Davies 1998). A more elaborate one-way nesting was reported in Beckers et al. (1997) where a 4.6-km resolution model of the Gulf of Lions is nested in a 16-km resolution model of the western Mediterranean. To facilitate a two-way scheme models of the same type are usually nested within each other. An example of two-way nesting is the Farvandsmodellen ( FVM) operational model of the Danish waters (Jensen et al. 1999) which uses ﬁve nested grids ranging from 9 n.mi to 1/3 n.mi in the Danish Straits. The system is based on the MIKE3 model (Danish Hydraulics Institute 1996). Fox & Maskell (1996) used a nested version of the GFDL model to obtain a better representation of fronts and eddies in the Iceland–Faeroe region. Metzner et al. (2000) used three nested grids as part of their simulation of seiches in the Baltic. Occasionally models of different type may be nested. For example, in Malabib et al. (2000) a ﬁne mesh 556 m resolution 2-D version of the ﬁnite difference TRIM model is used in the northern Adriatic, taking as its boundary input information from a large-scale ﬁnite element model of the entire sea. The model is used to look at the dynamics of semi-diurnal and diurnal tides in the region.

Transformations of an orthogonal grid To avoid possible problems with nesting, a single model can use a local mesh reﬁnement such as the telescoping grid used by Schmidt et al. (1998) centred on the Kattegat (Fig. 3), and the model of Sladkevich et al. (2000) centred on the Haifa region of Israel. Another approach is to use polar co-ordinates as in Roy et al. (1999), where the area of interest effectively becomes the centre of a circle and the mesh is aligned along the radii and arcs of increasing radius.

Curvilinear Several of the models with regular rectangular or spherical grids are now offering options based on a curvilinear co-ordinate transformation to focus on areas of interest. The HAMSOM, HYCOM, POM, VICTOR, SPbGNOME and NZB (Fig. 4) models were all reported as having a curvilinear option. Young et al. (2000) demonstrated a modiﬁed form of the DJM 60

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Figure 3 Telescoping grid of the entrances to the Baltic. Adapted from Schmidt et al. (1998: 354). By permission of Deutsche Hydrographische Zeitschrift.

Figure 4 Curvilinear grid of the NZB (North Sea Basis) model. By courtesy of E. de Goede, WL-Delft Hydraulics.

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model that uses a curvilinear grid to focus on ﬂuxes through the North Channel of the Irish Sea. Androsov et al. (1998a,b) used a curvilinear grid to examine the tides on the North Siberian shelf and Barents Sea. In an earlier paper, Androsov et al. (1995) used a curvilinear grid in the Strait of Messina and paid particular attention to the associated boundary problems. Bailey et al. (1995) investigated methods of curvilinear grid generation and Ly & Luong (1999) demonstrated a multiblock model based on POM which used nine blocks of orthogonal grid with eight curvilinear blocks to model the Mediterranean. Tejedor et al. (1999) used the SPbGNOME model with a curvilinear representation to examine the spatial pattern of the semi-diurnal tides in the Strait of Gibraltar and a further application of the model examined the tidal energy budget and ﬂow ﬁelds in more detail (Androsov et al. 2000).

Vertical co-ordinates In the vertical the type of co-ordinate to be used is not so clear. The choice of co-ordinate can inﬂuence many issues regarding methods of solution. The review by Grifﬁes et al. (2000) gives an overview of the general schemes used at present: z or geopotential coordinates, ρ- or isopycnal co-ordinates, σ- or terrain-following co-ordinates. There are also reﬁnements of these, notably double σ co-ordinates, s-co-ordinates and various functional forms as well as multi-layered schemes. Certain hybrid schemes have also been developed. All these types of co-ordinate have been used in European models and each of these systems will be brieﬂy examined in turn.

z-co-ordinates According to questionnaire responses, z-co-ordinate models are used very frequently. To a certain extent this may be due to the use of models derived from the GFDL/MOM series of models which use z-co-ordinates, but the HAMSOM, PROBE-Baltic, OCKE3D, TRIM3D, HIROMB, LODYC/OPA and MIKE 3 models also all use z-co-ordinates. Note that the zlevels are not necessarily equally spaced. Treguier et al. (1996) showed that non-uniform grids in the vertical are acceptable if a smooth stretching function is used. The advantages and disadvantages of the z-co-ordinate models are presented by Grifﬁes et al. (2000). One problem is the step like representation of the sea bed and this is considered in the shaved cell approach of Adcroft et al. (1997). The representation of the bottom boundary (BBL) in a z-co-ordinate model is also considered by Song & Chao (2000) who proposed an embedded bottom boundary layer (EBBL) scheme as an improvement of the BBL model of Killworth & Edwards (1999). Another problem of z-level models is highlighted in many papers. In the oceans, diffusion preferentially occurs along isopycnal surfaces which are generally inclined to the z-levels. Mathieu & Deleersnijder (1998) reported on the method of using a rotated mixing operator to diffuse along isopycnals, but noted that problems still remain because there was a need for a monotonic isopycnal diffusion scheme (see p. 70). Beckers et al. (1998, 2000) considered this problem in great detail and compared several approaches to the numerical discretisation; LINEAR, LINEAR1, LINEAR2, the standard method termed CLASSIC, COMBI, AMPMIN and FLUXCORR, each of which has individual advantages and disadvantages. 62

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σ-co-ordinates From the responses to the questionnaire, the σ-co-ordinate scheme was the next most popular: again this may be due to the use of the POM model and models derived from it. However, σ-co-ordinates are also used by SPbGNOME, the set of turbulence models of Xing & Davies (1996a), the COHERENS model and BOM. Berntsen & Svendsen (1999) used data from the SKAGEX project to compare the POM and BOM σ-co-ordinate models. The spacing need not be uniform. In Xing & Davies (1998b) 50 σ-levels were used, with increased resolution in the surface and near bed for the special case of examining internal waves. In Davies & Jones (1990) a logarithmic spacing via a transformation of the equations is used. Similarly, Kämpf & Fohrmann (2000) used special σ-co-ordinates within a rigid lid model to resolve a thin bottom boundary layer in order to trace sediment plumes. A full mathematical transformation of basic equations into σ-co-ordinates is given in Davies (1986). One of the well-known disadvantages of σ-co-ordinates is that the σ-levels will be inclined in an area of steep bottom topography. Any horizontal pressure gradients on these levels will acquire a vertical component and hence spurious circulation may result. This problem has engaged the minds of researchers (e.g. Deleersnijder & Beckers 1992). Burchard & Peterson (1997) discuss two hybridisation schemes between σ- and z-levels to reduce this problem. Kliem & Pietrzak (1999) use this hybridisation in a comparison of the POM and SPEM models and a laboratory experiment. Also Auclair et al. (2000b), using the SYMPHONIE model, gave an overview of the problem and devised a three-stage method using adjustment of σ-levels, control of the bottom topography slope parameter and some optimisation to reduce the effect. A model of the Gulf of Lions was used to demonstrate this technique. Lin & Falconer (1997) discussed the problem of σ-co-ordinates in estuary problems especially where drying takes place and recommended a ﬁxed grid. They demonstrated a 3-D layered model in which only the surface and bed layer have variable thickness and in which only a single layer takes part in wetting and drying. The 2-D equations are ﬁrst solved using an ADI scheme, and the depth mean velocity is obtained from integrating the layer mean velocity to give the pressure gradient. The layer integrated 3-D equations are solved using the pressure gradient to obtain new layer-averaged velocities.

Double σ-co-ordinates If the model bathymetry changes very signiﬁcantly, for example where the shelf and adjoining deep ocean are included in the same model, resolution in a simple σ-level model is lost near the surface as the levels spread out in the vertical in deeper water. Good resolution is desirable near the surface where atmosphere–ocean exchanges take place and a solution has been to use double σ-co-ordinates where a ﬁxed horizontal layer is speciﬁed with a set of σ-co-ordinates above and below. Stanev & Beckers (1999a) used the GHER model with such a system in the Black Sea with the ﬁxed layer at a depth of 200 m, with 15 non-uniform layers above and 10 levels below, the depths reaching approximately 2000 m. Stanev & Beckers (1999b) used the model to investigate the seasonal circulation of the Black Sea and demonstrated a very signiﬁcant interannual variability. River plumes being generally twolayer systems are also conveniently represented by double σ-co-ordinate schemes (Marsaleix et al. 1998). The TELEMAC ﬁnite element model can use σ- or double σ-co-ordinates or revert to a z-level type using collapsed layers. 63

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S-co-ordinates A reﬁnement of the σ-co-ordinate scheme in an attempt to maintain resolution in the surface layer is the s-co-ordinate scheme originally announced by Song & Haidvogel (1994). A functional form is used from point to point in the model, which deﬁnes the σ-levels ensuring resolution where it is needed. The POL3DB model (which is now part of the POLCOMS system) used σ-co-ordinates originally, but as the model has now expanded to include more deep-water areas adjacent to the northwest European continental shelf an s-co-ordinate option has been included. They are also used in the NAUTILUS ﬁnite-element model.

Isopycnal-co-ordinates As mentioned above, isopycnals form a preferred pathway for diffusion in the ocean and so a model based on isopycnal co-ordinates might be expected to have some advantages. The OSMOM model (Røed 1995) uses isopycnals and was developed from a one-layer reduced gravity model as reported in Hackett & Røed (1998). The present version consists of a stack of well-mixed layers and has been applied to a study of jets, ﬁlaments and eddies off the Iberian Peninsula. Another multilayer isopycnal model (now known as JENSEN) was used (Jensen 1998) to look at different open boundary conditions. No respondents to the questionnaire apparently used the well-known MICOM isopycnal model which originated at the University of Miami. However a development of the hybrid-co-ordinate derivative of MICOM, HYCOM is used at the NERSC (Nansen Environmental and Remote Sensing Centre), Bergen. Nevertheless, the use of MICOM is prescribed as part of the EU-DIADEM project which involves data assimilation studies. In the HYCOM form the isopycnal model reverts to a σor z-level version near the surface in coastal or shelf seas. Onken & Sellschopp (1998) used the quasi-isopycnal model (BB) of Bleck & Boudra (1981, 1986). In the BB model the co-ordinate surfaces coincide with isopycnals wherever and whenever this is possible without violating a minimum layer thickness, otherwise the co-ordinate surfaces assume a quasi-cartesian character. Another hybrid model is reported in Ryabtsev & Shapiro (1998). This model is quasi-isopycnal and consists of an upper mixed layer and internal layers. Only the upper mixed layer is allowed to exchange with a lower layer. It is stated that prescribing the basic stratiﬁcation in fact governs the discretisation. A further recent European isopycnal model is the OPYC model, which is a development by Oberhuber and which has been applied to the circulation of the Arctic Ocean (Holland et al. 1996).

Functional form An alternative method is not to divide the vertical at all but to use a functional representation. This representation could be any arbitrary functional form, continuous functions such as Chebyshev polynomials or cosines (Davies 1980) or piecewise functions such as Bsplines (Davies 1983). In these functional methods the current proﬁle is then the sum of a number of modes through depth. However, spectral methods, where the basis funtions are eigenfunctions of a proﬁle of eddy viscosity, have distinct advantages. A review of these methods is given in Davies et al. (1997a). Ng et al. (1996) use empirical orthogonal function (EOF) analysis of ADCP proﬁle observations to justify the use of eigenfunctions derived from simple vertical eddy viscosity proﬁles in the Galerkin method in 3-D tidal current 64

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models. In Davies et al. (1997c,d) the model scheme allows the number of modes to be increased locally where required. A similar scheme was used by Davies & Kwong (2000) where a model of the northwest European continental shelf, using 28 tidal constituents, was demonstrated. This approach has distinct advantages for wind-induced ﬂow in shallow regions taking account of wave-current interaction in the bottom boundary layer (Jones & Davies 1998). It should be mentioned here that functional representation in the vertical may be used to represent observational data. Bruno et al. (2000) examined the vertical proﬁles of data from current meters in the strait of Gibraltar using both EOF (Empirical orthogonal function) and DMD (dynamical mode decomposition). In this way modes could be an additional way of comparing models and observations.

Vertical discretisation, ﬁnite difference form In the survey, the question was posed as to the type of discretisation used in the vertical. Finite difference schemes were the most popular with a staggered grid where scalars and horizontal currents were prescribed at different levels. There were some exceptions, the σco-ordinate model of Davies & Xing (1995), used subsequently in Xing & Davies (2001b), is non-staggered in the vertical. Davies & Jones (1991) compared the advantage of using a staggered or non-staggered grid with a number of turbulence energy schemes but found that the effects were generally small.

Vertical discretisation, general In some cases a mixture of layers, functional expansions and ﬁnite differences are used. For example, in an early paper studying shelf waves, Heaps & Jones (1985) reported a threelayered model with a functional representation in each layer. This scheme was effectively a kind of ﬁnite element discretisation in the vertical but in the horizontal a ﬁnite difference grid was used. Similarly, Davies (1982) used a continuous representation of density and current in the vertical with a ﬁnite difference in the horizontal. Indeed the functional form need not be conﬁned to the vertical. Bosseur et al. (2000) described a method based on Galerkin mode decomposition where not only the vertical but the horizontal ﬂows were represented. A quasi ﬁnite-element grid was used. The method was demonstrated but at present only a ﬁxed eddy viscosity can be used. Two more vertical discretisation schemes should be mentioned. Fortunato & Oliveira (1999) made use of an adaptive grid in a vertical 1-D NUBBLE-based model. It enabled the grid to be reﬁned at an arbitrary point in the vertical to improve represention of pycnocline regions. It is a ﬁnite element type model, and of the three schemes available, h (adding nodes), p (increasing the order of the interpolation functions) or r (moving the nodes), the r-method gave the better accuracy. In the r-method the nodes are moved vertically based on velocity gradients between consecutive nodes. Finally, Martins et al. (2001) demonstrated a new concept for vertical discretisation based on a ﬁnite volume approach, which gives a complete separation between the hydrodynamic variables and the geometry.

Free surface, rigid lid In early numerical modelling of the oceans, such as in the Bryan–Cox–Semtner model (Bryan 1969, Semtner 1974, Cox 1984), a rigid lid scheme was applied to enable the use of 65

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fairly long time steps. Killworth et al. (1991) developed a free surface form of the code. It might be expected that in coastal regions a rigid lid scheme would experience problems in representing the high tidal ranges and general wind setup. From the questionnaire responses it was interesting to see that the rigid lid form was nevertheless used in a few models but almost all of these were uses of the Bryan–Cox–Semtner model itself or in idealised studies. The Zuur (1991) model (PROSPER) uses a rigid lid and is based on the SOMS model (Dietrich et al. 1987) and has been applied to a lake study (Zuur & Dietrich 1990). Paluszkiewicz et al. (1995) used the Semtner (1974) model to look at wind-driven variability of the Amazon river plume. Pinardi et al. (1995) and Sheng et al. (1998) distinguished between the two forms of rigid lid model, the GFDL volume stream-function formulation, and a sea surface pressure formulation as used in DIECAST. Pinardi et al. (1995) deduced implications for altimetric data assimilation. Deleersnijder (1995), however, questioned some assumptions of this paper and put forward a suggested variational method.

Limited dimensions Having discussed co-ordinate systems and model discretisation the overall dimensionality of a model should be discussed. Most general models offer a 3-D description of the domain of interest. However, reduced dimension (i.e. single point, box, 1-D and 2-D) models apparently do have a wide and useful applicability.

Box The simplest dimensional form of model is the single point or box model. Although no spatial information is produced these models can yield the time evolution of a process, for example, Rippeth & Simpson (1996) demonstrate a box model of the Clyde Sea to examine episodes of complete vertical mixing. Pohlmann et al. (1999) used a cluster of four boxes to examine phosphate dynamics in the German Bight, Karaca et al. (1999) used a two-component box model to simulate the salinity evolution of the Black Sea after the opening of the Bosporus approximately 7500 years ago. Nielsen (1995) used a model of no less than 91 boxes covering the whole northeast Atlantic to examine the time and space scales of radioactive tracers. Rosón et al. (1997) used a cluster of eight two-layered boxes to calculate residual ﬂuxes in an estuary. This particular model has several extensions which allow detailed examination of such items as non-stationary terms in the advection-diffusion equation. An interesting comparison of the ability of a box-type model to both simulate realistically the circulation of a system and to give an insight into its main features was provided by Deleersnijder et al. (1998). In this paper, a simple two compartment model, one dominated by diffusion, the other by advection, showed the same circulation features as a 3-D POM model simulation of the same area (Mooers & Wang 1998). Pinot & Ganachaud (1999) used an inverse ﬁve box model to examine the circulation of the Balearic Sea. (This was followed by a complementary numerical model study, Pinot et al. 1999.) Carlsson (1998) used a quasi-stationary, barotropic, box model of three sub-basins connected by straits, to represent the Baltic. The model predicted 85% of variance of the system due to wind stress, air pressure and density. Also in the Baltic, Omstedt & Rutgersson (2000) used a 13 sub-basin model to examine the water and heat cycles of the Baltic. Yet another type of box model is 66

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the classic tidal prism model which considers the tidal ﬂow into and out of estuaries. Luketina (1998) demonstrated the deﬁciencies of the original model form and developed two new improved versions.

1-D There are many 1-D models in the literature that have been used successfully in coastal and shelf seas. Luyten (1997) demonstrated analytic solutions from a 1-D two-layer model and then used a numerical point model with an advanced turbulence closure. As seen earlier the adaptive grid of Fortunato & Oliveira (1999) is used in a vertical 1-D model. Burchard (1999) examines a new method for forcing 1-D water column models of turbulent tidal ﬂow and applies this scheme to the Scheldt estuary in the Netherlands. Eknes & Evenson (1997) use a variational formulation in a 1-D Ekman formulation model for an inverse calculation using an iterative scheme to achieve a better estimation of poorly known parameters. Warrach (1998) used a 1-D model to examine the effect of various turbulence closure schemes (see p. 75) and surface forcing on the vertical temperature structure in the southern North Sea. Tett (1990) developed a three-layer 1-D vertical model for vertical and microbiological processes for shelf seas. A version of this latter model eventually became a component of the COHERENS model. Tusseau et al. (1997) demonstrated a coupled 1-D model of Ligurian Sea. This model calculates biological production rates and incorporates the submodels AQUAPHY and HSB. A group of 1-D models may be combined. Gustaffson (2000a,b) used 11, 1-D sub-basins to represent the Baltic. Lane & Prandle (1996) examined the interannual temparature variation of the North Sea with what is in effect an array in the horizontal of 1-D vertical models creating a quasi 3-D model. Similarly, Elliott & Li (1995) with a grid of 1-D mixed layer models using a Mellor-Yamada level 2 turbulence closure scheme (see p. 75) showed the main features of the annual temperature cycle of the northwest European shelf seas under stochastic wind forcing. Occasionally a 1-D model may be embedded in a 2-D or 3-D model to examine particular processes. Chapalain & Thais (2000) employed a 1-D model using the horizontal pressure gradient derived from a 2-D model to simulate turbulence and suspended sediment in the eastern English Channel. Oguz et al. (1999) used a 1-D plankton and nitrogen model of the Black Sea, the hydrodynamics coming from a 1-D version of the POM model (Oguz et al. 1996). The model simulated the main observed seasonal and vertical characteristics of the upper sea. Xing & Davies (1999b) reported on a 2-D cross-sectional model of the Scottish shelf used to study both across and along shelf ﬂows together with changes in bottom boundary layer mixing due to along-shelf ﬂow. This model incorporated a 1-D model simulating the time variation of thickness of bottom boundary layer, dependence on the slope angle and an upwelling- or downwelling-favourable along-slope ﬂow.

2-D (slice) 2-D vertical, sometimes known as “slice” models are of interest in examining particular systems as seen above in Xing & Davies (1999b). Souza & James (1996) used a 2-D version of an existing 3-D model with some changes to the time-differencing and advective schemes to examine tidal straining in the Rhine plume. Xing et al. (1999) used a 2-D slice model of 67

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the Scottish shelf with various turbulence closure schemes, and showed that meteorology is responsible for the basic stratiﬁcation and that surface and internal tides are important for mixing. As mentioned above several “slice” models were used in examining continental shelf waves and patterns of cross-shelf sediment deposition. Amin & Huthnance (1999) speciﬁed horizontal and vertical diffusivities and settling velocities to calculate the distribution of suspended particulate matter in a section across the continental shelf and slope. Mattson (1996) used a 2-D sectional three-layer model for three water types including frontal dynamics, to study inﬂows, outﬂows and salt ﬂux in the Öresund region.

2-D ( horizontal) and 3-D There are very many models that are 2-D in the horizontal. Such models are particularly appropriate for general basin-wide tidal propagation problems. For example the 2-D model of Tsimplis et al. (1995) successfully reproduces the tides of the Mediterranean and illustrates the importance of the tidal input from the Strait of Gibraltar and the equilibrium tide. However in shallower regions with ﬁner resolution a 3-D description may become necessary as shown using the WCM model (Davies & Jones 1992). The large number of other 2-D models will not be considered separately here, but as they arise in the rest of the review. 3-D models are also very common indeed and will be dealt with later in this review. However, it should perhaps be commented that there are ways of extracting 3-D information from a 2-D model. Davies (1990) and Nøst (1997) show how it may be possible to extract a vertical proﬁle from a 2-D model. Switching dimensionality when necessary is demonstrated in two papers. In de Kok (1996) a 2-layer model, which is the simplest form of 3-D, is demonstrated. The grid moves with the vertical position of the density interface in a model of the Rhine plume. In a subsequent paper (de Kok 1997), a 3-D ﬁnite difference tidal model is used in a 2-layer simulation of the Rhine plume. Calculation proceeds with two layers until the density difference between the layers is small when the model then locally switches to 3D.

Time Discretisation Time increment – the CFL criterion In running a conventional numerical model, using an explicit time stepping approach, one constraint universally recognised is the CFL (Courant–Friedrichs–Levy) criterion, which imposes a maximum limit on the time step that can be used in a model of given grid size and given maximum depth. This stability condition is related to the speed of propagation of free surface gravity waves. If the CFL criterion is exceeded, instability generally results. However, the CFL criterion can be exceeded when a semi-implicit or fully implicit solution is used but can lead to phase errors for tidal propagation (Davies & Gerritsen 1994). In shelf models in the horizontal, the CFL criterion is related to barotropic motion and the time step is therefore related to the maximum depth in the model. In the vertical, there are time step constraints related to the diffusion of momentum by the vertical eddy viscosity term. In shallow water with high levels of turbulence, hence high vertical eddy viscosity, and a ﬁne vertical grid if an explicit method is used, a time step of fractions of a second would be required. The vertical diffusion term is, therefore, always discretised using an implicit or 68

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semi-implicit method. Similar problems can occur with bottom friction in shallow water and requires the use of implicit solutions, particularly in models using a functional approach in the vertical (Davies & Aldridge 1993). For momentum advection, problems can occur when the time step exceeeds the maximum current. Methods that have been developed for advection of sharp density gradients can also be used for momentum advection. James (2000) has recently reported how the use of the PPM advective scheme (see p. 70) may overcome the limitation that vertical momentum advection imposes on the time step. For baroclinic motion, however, the speed of propagation of the internal waves is signiﬁcantly slower than that of the free surface gravity wave. Advantage is taken of this property by splitting the calculation of the barotropic and baroclinic modes in a model to reduce computational expense. Note that even “steady state” models usually have to be run forward in time from rest until a steady state is achieved and they are, therefore, also subject to the various time step criteria.

Steady state or time evolving From the responses to the questionnaire the great majority of models used at present are time-evolving. Steady state models, or models whose time-evolution is periodic or harmonic tend to be used in more idealised situations but the elimination of the time element may enable certain processes to be more clearly discerned. For example, a recently developed 2-D vertical model (J. M. Huthnance, pers. comm.) examines cross-shelf circulation associated with forced along-slope ﬂow in a steady state situation. Another steady state solution in a 2-D vertical model, reached by iteration, examines sediment patterns across the slope in the region of the Goban Spur (Amin & Huthnance 1999). A harmonic solution, again for a 2-D vertical model looking at stratiﬁed/baroclinic coastal trapped wave forms, is the BIGLOAD2 model of Brink & Chapman (1985, 1987). A different formulation considering the same problem has been developed by Huthnance (1978). A further harmonic model to simulate continental shelf wave forms was developed by Huthnance (reported in Cartwright et al. 1980) based on the formulations of Caldwell et al. (1972). A cross sectional sediment transport model similar to Amin & Huthnance (1999), although with the inclusion of a turbulence energy sub-model and time evolution, has been recently developed by Davies et al. (in press). Other earlier steady state models such as the diagnostic model of Sarkisyan (Stanev & Nikolow 1979, Trukhchev & Stanev 1983) simulated the general background circulation of a sea area, in this case the Black Sea. More recently, Omstedt et al. (1996) examined the ice-ocean response in the Baltic due to variable winds using both an analytical and a numerical model, and sought to achieve a steady state response. Very recently a full 3-D ﬁnite element model (Maidana et al., in press) was used in a steady state version to study the general background non-oscillatory circulation in the area of the Ebro mouth. It must also be pointed out that sometimes steady state responses are developed in hydrodynamic models and the ﬂows passed to advection-diffusion models looking at long-term dispersion processes (see p. 73).

Initial ﬁelds If a model run is to simulate a real period then it usually requires an initial spin-up run to provide the starting point for the realistic simulation. This “running in time” can be expensive in computer time, although Auclair et al. (2000a) suggested an inverse method of 69

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determining initial conditions from available observations to avoid the necessity of a long run in of a computationally expensive model. Usually near-coastal models are simulating areas of high dissipation so a long initial run is not necessary. However, as models move their boundaries further into the deep ocean, dissipation reduces. Hence more attention will need to be paid to initialisation ﬁelds in future because a poor description can lead to spurious circulation which may take a long time to disappear, if at all.

Time stepping From the responses to the questionnaire the time-stepping methods varied widely. Some were associated with standard models; the POM model uses a leapfrog scheme, for example, with the possibility of mode splitting. The OSMOM has a leapfrog scheme with an Asselin ﬁlter. The model of Xing & Davies (1996e) has a time split between barotropic and baroclinic ﬂows and an implicit solution for vertical diffusion of momentum and density. HAMSOM is usually synchronous semi-implicit although there are other options, the level of the implicit scheme can range from Crank–Nicholson to fully implicit.

Advection of momentum and tracers Momentum The advective schemes used in hydrodynamical modelling are very important indeed. Advection is not only needed for momentum and density but for tracers of all kinds which may be of biological origin, chemical species or sediment in suspension. Furthermore, advection may also be used to transport the unresolved sub-grid scale turbulence that is generated in many turbulence closure models. This latter possibility is included in many models such as Xing & Davies (1996b) and in the COHERENS model, although Ruddick (1995) argued that this effect is small in most oceanographic applications. Haidvogel & Beckmann (1998) listed the desirable properties of advective schemes as monotonicity (no production of spurious wiggles in tracer concentration), positive deﬁniteness (no production of spurious extrema in tracer concentrations), low implicit diffusion, preservation of invariants (energy, enstrophy, tracer variance), accuracy, ease of implementation and low computational cost. Subsequently, Haidvogel & Beckmann (1998) commented that no single discrete advection scheme is known to possess more than a few of these desirable properties. The response to the questionnaire reﬂected this disparity, many different advective schemes are used by modellers in Europe, and there was no consensus. Haidvogel & Beckmann (1998) also commented on the schemes used by the models under review in their paper. Some use the centred advection scheme which can lead to negative concentrations but, as it is easily implemented and not too diffusive, it is quite often used. It is listed as being used by DieCAST, MOM, POM, SCRUM and SPEM models. A low order upwind algorithm avoids some of the problems of the centred advection scheme but can have rather high diffusion of tracers. The HAMSOM, ISPRAMIX and M3D models considered in the paper use this form. In the review by James (1996) there is a detailed discussion on the need for advective algorithms that preserve sharp gradients which are, of course, essential in sea areas where stratiﬁcation and frontal zones occur. In James (1996) two particular schemes are presented, 70

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the Total Variation Diminishing (TVD) and the Piecewise Parabolic Method (PPM) which originally derives from work in gas dynamics (Colella & Woodward 1984). Both schemes are monotonic and the PPM scheme has particularly low numerical diffusion. In two or three dimensions a directional splitting is used to avoid the artiﬁcial vertical diffusion that can occur in σ-co-ordinate models in areas of sloping sea bed. These two schemes are becoming increasingly popular, although at computational cost. In James (1996) other advective schemes are mentioned such as the Lax-Wendroff method which, however, lacks positivity and can produce spurious ripples near a sharp front. To try to retain a sharp front, several methods have evolved such as ﬂux-corrected transport (FCT), which has been used as an alternative method in MOM, the self-adjusting hybrid scheme (SAHS) and the antidiffusive velocity method of Smolarkiewicz (1984), which is refered to by an acronym in Hecht et al. (1998) as MPDATA (Multidimensional Positive Deﬁnite Advection Transport Algorithm). This latter scheme is used in MICOM. James (1996) also pointed out the use of limiter functions which ensure ripple free (TVD) behaviour, MINIMOD, Monotonic upstream scheme for conservation laws (MUSCL), MONOTONIC and SUPERBEE. In Souza & James (1996) a 2-D version of the James 3-D scheme is presented to examine tidal straining in the Rhine plume. This scheme incorporates some changes to the formulation of the advective scheme but the directional splitting is retained. There are further limiter schemes available, KOREN, SMART, van Albada and van Leer (Brunet et al. 1999). The use of the TVD scheme to preserve sharp density gradients is shown by Xing & Davies (1998b) to be important in internal tide generation. There are further advective schemes. The QUICK scheme was announced by Leonard (1979) and was used fairly widely, sometimes adapted for speciﬁc uses (e.g. Chen & Falconer 1992, 1994). This scheme was followed by the quadratic upstream interpolation for convective kinematics with estimated streaming terms, QUICKEST (Leonard 1995), which is a third-order-accurate scheme which maintains fronts and also limits the problem of wiggles at fronts. However, it is not monotonic. Two monotonic schemes have been presented: the monotonic second-order upwind, and the application of a limiter, ULTIMATE (Universal limiter for transient interpolation modelling of the advective transport equations) (Leonard 1991). From this development has arisen the ULTIMATE–QUICKEST scheme. Leonard (1995) gave an overview of the order of accuracy of QUICK and related convection–diffusion schemes, and compared other schemes like the single point upwind difference scheme (SPUDS). Webb et al. (1998) demonstrated that the QUICK operator could be split so that it could be naturally incorporated in the Bryan–Cox–Semtner code. Vested et al. (1996), using a combined hyrodynamic and ecological (12 state variable) system, estimated the effects on ecological simulations and advantages of different systems. The tests were based on the Danish operational storm surge system and the UPWIND, QUICKEST, MSOU and QUICKEST–ULTIMATE schemes were used. There are some further issues in the advection of momentum. Bell (1998) examinined the problem in z-level models that the velocities used to advect momentum over steps in the bathymetry are inconsistent with main velocities. Five schemes are evaluated including two new ones. The Webb (1995) scheme appears to give the best results. In the nearshore region Deigaard et al. (1998) demonstrated that the combination of a vertical orbital wave motion and mean current gives a periodic variation in the horizontal. This process can affect momentum exchange. Shapiro et al. (1997) also proposed a mechanism for the motion of drifters whereby the advection of an eddy may result in the capture or release of Lagrangian ﬂoats. 71

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Tracers It is not necessary to use the same advective schemes for tracers as for momentum. In general, however, from the questionnaire most modellers appeared to use the same schemes. For example, the OCKE3D model uses a TVD scheme with a second order SMART limiter for both. In the MIKE21 system the QUICKEST–SHARP scheme was used for both momentum and tracers. In certain instances such as the VICTOR model (George & Stripling 1995) clouds of particles simulated tracer advection. Xing & Davies (1996e) also reported the use of a centred difference method for the advection of momentum and a TVD scheme for tracers. Hecht et al. (1998) gave a review of tracer advection schemes based on the GFDL model. The methods used include centred in time, centred in space, donor cell upwind differencing scheme, FCT, QUICKEST and MPDATA. A new general method is proposed for the adaptation of a second-order accurate application of a 1-D forward in time advection scheme to three-dimensions. Tracers such as pollutants or salinity are, however, generally subject to both advective and diffusive processes. There are separate speciﬁc advection–diffusion models that take their advective ﬂows from an external full hydrodynamic simulation with speciﬁed diffusion coefﬁcients. These models can be very useful in the simulation of long-term dispersion of materials where a full hydrodynamic simulation would be prohibitively expensive. This class of models will be discussed after the following section on horizontal diffusion.

Horizontal diffusion of momentum and tracers From the results of the questionnaire it was interesting to see if horizontal viscosity was used and, if so, what kind. In numerical models, horizontal viscosity is employed for two reasons. First, it may be required for numerical stability by controlling small-scale noise that builds up by nonlinear effects which must be removed, and second, it may parameterise the exchange of momentum by unresolved motions. There are several schemes for determining the value of the horizontal viscosity coefﬁcient and including its effect in a model. The simplest is to choose a coefﬁcient and use the same value everywhere for all time. Other formulations are to relate it to ﬂow and/or local water depth, or local horizontal shear. A range of possibilities exist for determining its value from simple to more complex. The survey showed that a popular scheme is the Smagorinsky (1963) formulation of horizontal eddy viscosity which is related to the deformation or strain rates of the resolved velocity ﬁeld. The most popular implementation of horizontal viscosity was in terms of the Laplacian (∇2operator) formulation. There were a few uses of the biharmonic (∇4) formulation and some mixed or hybrid schemes. Proctor & Davies (1995) highlighted the need for a better description of horizontal viscosity and Xing & Davies (1998a) used a biharmonic form of viscosity. Crise et al. (1998) also used a biharmonic horizontal diffusion in modelling the nitrogen cycle in the Mediterranean. There are other methods of horizontal diffusion such as the Shapiro ﬁlter (Shapiro 1970), which has the advantage of being quite high order and can be one-sided near model boundaries. However, no model surveyed in the questionnaire used this or any other distinct scheme for horizontal diffusion. In addition, a few random walk schemes were used to quantify horizontal diffusion. It should be mentioned at this point that in deep-ocean models there is transport and diffusion of momentum and tracers by mesoscale eddies that may be unresolved in coarse 72

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grid models and therefore some form of parameterisation is needed. Roberts & Marshall (2000) conﬁrm that the Gent & McWilliams (1990) scheme is particularly successful, but Grifﬁes et al. (2000) comment that it has taken some time for this method to become regularly used in models. Although generally used in the deep ocean, this method may also be used in European seas. Haines & Wu (1998) use this method in a model of the Mediterranean (see p. 89).

Advection–diffusion models, dispersion of tracers An example of an advection–diffusion model using hydrodynamic input from a 35 km resolution model of the northwest European shelf was presented by Prandle (1984) to examine radionucleide dispersion. In similar fashion, an 8-km resolution model based on the 2-D model reported by Jones (1999) provided hydrodynamics for a model of salinity of the same area (Jones & Howarth 1995). Similar schemes were at the basis of the NOSTRADAMUS model (Tappin et al. 1997a,b) which looked at the spatial distribution of metals in the North Sea and deduced implications for the behaviour of lead. Two other advection–diffusion models were DYMONIS for the Irish Sea (Le Gall et al. 2000) and DYMONNS (Hydes et al. 1996) for the North Sea, both biogeochemical models. The similar method of Dyer & Moffat (1998) used residual ﬂows from an existing hydrodynamic model to examine sediment ﬂuxes in the East Anglian sediment plume in the southern North Sea. Prandle et al. (1996) compared modelled and observed ﬂuxes of waters and metals through the Dover Strait. The technique can be applied in an inverse fashion. McManus & Prandle (1997) used an advection–diffusion model to simulate the effect of individual sources of trace metals and then ﬁtted a multiple linear regresssion to observations to determine the actual output of these sources. The advection of tracers can also be performed in a Lagrangian or particle tracking method. Dick & Schönfeld (1996) used a 3-D baroclinic model with a Lagrangian module to compute water transports and mixing times. They showed that just estimating turnover from a tidal prism model can be inaccurate. Gomez-Gesteira et al. (1999) also used a particle tracking model, PARTIC, in conjunction with the MOHID model 2-D version, to assess residence times in two Spanish rivers. Breton & Salomon (1995) described a “barycentric” particle tracking system to compute 2-D and 3-D long-term dispersion in tidal environments with the aim of simulating radionucleide dispersion in the English Channel. Salomon et al. (1995) continued this investigation estimating transit times from a source at Cap de la Hague. The dispersion of radionucleides is a prime subject of modelling methods. Schönfeld (1995) used the BSH model with a Lagrangian scheme to simulate radionucleide dispersion in the English Channel. Staneva et al. (1999) used a version of MOM to examine the dispersion of 137Cs in the Black Sea due to weapons testing and the changes in the pattern of the radionucleide due to the Chernobyl event. Periáñez (1999, 2000) used a 3-D model to simulate the dispersion of radionucleides in the eastern Irish Sea. As the species include plutonium, which might be expected to settle, the model also contains the dispersion, erosion and settling of sediment. As mentioned earlier, the 91 box model of Nielsen (1995) was used to point out the time and space scales needed to model radionucleides. Of course, advection–diffusion may not be simply a hydrodynamic matter. In Harms (1997) and Harms & Karcher (1999) the HAMSOM model is coupled with thermodynamic and dynamic sea-ice models to simulate the seasonally variable circulation 73

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of the Kara Sea, as well as to investigate the fate of possible leakage from discarded radionucleides. The concepts used in dispersion models may sometimes need explanation through the medium of modelling. Luff & Pohlmann (1995) used a HAMSOM 3-D shelf-sea model to feed a North Sea model to distinguish between the concepts of ﬂushing time, turnover time and half life. The concept of residuals also may be subject to discussion and explanation. Delhez (1996) examined different approaches to the long-term advection of passive contaminants using a 2-D model derived from GHER and concluded that the use of derived residuals can produce satisfactory results and discussed the best method of obtaining them. Lagrangian residuals are not applicable, Eulerian residuals are inadequate when tidal nonlinearities are important and so a ﬁrst order Lagrangian residual scheme was introduced and was shown to be a good solution in tests on the northwest European Continental shelf. Another concept in dispersion is that of “age” which is deﬁned as the time elapsed since the particle under consideration left a location at which its age is prescribed as zero. This process involves advection and diffusion as well as production and destruction. This concept is examined in detail in the modelling context in Deleersnijder et al. (2001). Several applications are described including the dispersal of radionucleides in the English Channel with hydrodynamics coming from the GHER model. It should be mentioned that there are alternative particle tracking methods based on statistics rather than originating directly from the hydrodynamics. The random walk method is used in models by Riddle (1996, 1998). De Swart et al. (1997) tested the use of a random walk model to calculate dispersion coefﬁcients for an estuary. However they found that excessive dispersion up to four times larger than from observations may result. Van Dam et al. (1999) used a scaled random walk (SRW) method in a model simulation, compared with tracer experiments to estimate the importance of vertical shear dispersion, variations in horizontal water movements and 3-D turbulence. Korotenko (2000) used a hybrid model based on the Monte-Carlo method to forecast transport processes of matter in coastal oceanic fronts. Naturally, biological or biochemical tracers may be subject to dispersion. Bergamasco et al. (1998) used two nested POM models to provide transports for a biochemical model in the Venice lagoon area. Some apparently unusual results seemed to be conﬁrmed by observations. In Lenhart & Pohlmann (1997), an advection–diffusion model is described based on the ICES box representation of the North Sea. The advection is calculated by a model and the vertical diffusion from stored values of vertical eddy viscosity. The coefﬁcient of horizontal diffusion is related to an effective diffusion time approximately equal to half the M2 period. Ibàñez et al. (1999) reported an improvement to Knudsen’s two-layer model of the advective ﬂuxes of water and salt in three-layer salt wedge estuaries. This new model gives a more accurate estimate of advective ﬂuxes in the low tide Ebro river estuary. It is of interest to compare results from advection–diffusion models. For example, Dick et al. (1999) calculated budgets of energy and matter between the Wadden Sea and German Bight as part of a project that includes ﬁeld measurements. Comments were made on the different estimates from the BSH model and the Pohlmann (1996a) model. Although they are generally close, differences did arise from different forcing and different resolution. The experiments showed the Wadden Sea acts as an area of decomposition of incoming organic material. A considerably more elaborate intercomparison between advection–diffusion models forms the basis of the NOMADS and NOMADS2 projects. A comparison of an idealised test case from the ﬁrst of these projects is presented by Tartinville et al. (1998). However 74

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this project also carries out individual sensitivity studies and realistic simulations, and importantly, evaluates mathematical tools for intercomparison.

Vertical sub-grid scale parameterisation In most models unresolved sub-grid scale motions in the vertical must be incorporated in a physically realistic manner because they make their effects felt through such effects as internal friction (eddy viscosity), and mixing (eddy diffusion) upon the larger scale motion. From the questionnaire responses it was clear that there was no consensus on the “best” scheme to use. This is perhaps not surprising because the parameterisation of turbulence is a major problem in physics, a comment emphasised by Ruddick et al. (1995). The characterisation of turbulence is therefore the subject of many studies including the EU–CARTUM project. Although highly complex schemes may be necessary for certain branches of physics, in the modelling of coastal and shelf seas such complexity is not generally necessary and cannot be supported by the necessary complex ﬁeld measurements. Thus a few distinct methods have become common. However, there does not seem to be agreement on one particular scheme or indeed whether complex schemes are always necessary. Sometimes a simple formulation may be sufﬁcient. A brief explanation of the main terms for the non-specialist may help. The very simplest form of sub-grid parameterisation is to use a constant eddy viscosity/diffusivity that is uniform in space and time. The next level is to specify an eddy coefﬁcient (viscosity or diffusivity) that is related to the ﬂow or to the local velocity shear. Stratiﬁcation can have the effect of reducing eddy viscosity and eddy diffusion and so a set of relations involving the Richardson number are sometimes used. A more complex method, which is therefore more computationally expensive, is to actually calculate the production and dissipation of turbulence using one of several methods. A popular scheme is known as the k-ε model which can have several levels of complexity and a review of these is given by Luyten et al. (1996) An alternative is the use of the q2-q2l or k-kl methods. Davies et al. (1997b) describe the difference between the q2-q2l and the k-ε methods and a detailed investigation using an idealised model may be found in Baumert & Peters (2000). A review of the k-kl schemes may be found in Ruddick (1995) and Ruddick et al. (1995). The hierarchy of models is based on a set announced by Mellor & Yamada (1974) and are hence referred to as MY methods from level 1 to level 4. The level 2.5 has become particularly popular and was the most common form of the MY schemes reported in the questionnaire responses. Many reﬁnements of these basic schemes have evolved. The MY schemes have an equilibrium version in which there is a balance between turbulence production and dissipation, although this can give rise to instabilities (Davies et al. 1995). Deleersnijder & Luyten (1994) demonstrate the advantages of the quasi-equilibrium form of the MY level 2.5 turbulence scheme, which is based on the inclusion of stability functions from Galperin et al. (1988) to form the more robust Mellor–Yamada–Galperin (MYG) scheme. Yet further schemes may be considered. The Kochergin parameterisation of eddy viscosity (Kochergin 1987) is used within the HAMSOM model (Pohlmann 1996a). The algebraic stress model (ASM) is described by Sajjadi & Aldridge (1995). This is a compromise between the realitive simplicity of the k-ε method and the complexity of a differential second-moment closure (DSM) scheme. Another “intermediate” approach now being applied is the so-called “KPP model” (K–Proﬁle Parameterisation) (Large et al. 1994), which uses an heuristically 75

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deﬁned vertical turbulent diffusivity proﬁle. The KPP scheme is often associated with the MICOM and HYCOM models.

Intercomparison of turbulence schemes With such a number of turbulence schemes available it is not surprising that a great many papers in the literature are devoted to comparing the different techniques. Burchard & Peterson (1999) compared the k-ε and MY 2.5 model, given in the same notation and demonstrate similar behaviour. Burchard et al. (1998) compared systematically the twoequation MY and k-ε models. Both models contain prognostic equations for turbulence and a length scale parameter. It was found that the k-ε gives a larger phase lag between currents and turbulent dissipation than the MY. Both need internal wave parameters to predict correctly turbulence dissipation. The stability functions which are used as proportionality factors for calculating eddy viscosity and eddy diffusivity have a stronger inﬂuence on the performance of the turbulence model than the length scale. Luyten (1997) presented analytic solutions from a 1-D two-layer model. Then he showed a corresponding numerical model, which is a point model with advanced turbulence closure. It is a q2-q2l model similar to a MY 2.5, ﬁrst presented by Luyten (1996). The differences between the analytical and numerical schemes are ascribed to nonlinear interactions between the turbulence and the effects of the current shear and stratiﬁcation. This latter point was also considered by Burchard & Baumert (1995) who examined the interaction between stratiﬁcation and turbulence. A standard and an advanced k-ε model are derived theoretically. Using these two schemes, a comparison was made with a MY level 2 and a modiﬁed version of the Kochergin turbulence scheme. On comparison with a dataset (FLEX ’76) the two k-ε methods performed best. Sajjadi & Aldridge (1995) examined turbulent ﬂow over steady bed forms in 2-D and concluded that the k-ε and DSM schemes produced satisfactory results but there were some deﬁciencies with the k-l method. They produced estimates of form drag and skin friction used for permeable media and comment that their model is suitable for very non-uniform grids. Xing & Davies (1996c), using a model of the Irish Sea, investigated a q2-q2l and a k-l one equation model where the mixing length may be related to the integral of the turbulence energy density following the formation of Blackadar (1962), or two forms of algebraic mixing length due to Smith (1982), or Johns & Xing (1993), the JX scheme. Subsequently, Xing & Davies (1996d), using a 3-D σ-co-ordinate model with two turbulence closure schemes, a two-equation q2-q2l and the simpler JX model, showed that ﬂow along shelf edge area to the west of Ireland and from the Irish Sea, together with wind can have major effects. Xing et al. (1999) in a 2-D slice model compared the turbulence closure schemes used in Xing & Davies (1996d) with a scheme based on an eddy viscosity related to Richardson number. The results showed that the latter scheme had a tendency to overestimate the vertical diffusion. Lee & Davies (1999), using 2-D and 3-D models of the Irish Sea, examined the inﬂuence of open boundaries and vertical eddy viscosity. Three eddy-viscosity formulations were used; two-equation turbulence, ﬂow dependent and constant. One conclusion was that the optimal bottom friction prescription can depend on the eddy viscosity parameterisation selected. Davies & Hall (1998) investigated the sensitivity of current proﬁles to momentum diffusion in a model of the North Channel of the Irish Sea using one-equation and twoequation turbulence schemes. 76

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Pohlmann (1997) commented that in 3-D the Kochergin (1987) scheme is equivalent to MY level 2. It is validated against data from the FLEX ’76 set of observations. Warrach (1998) described a 1-D model representing the station CS in the North Sea that was occupied during the UK NERC North Sea Project (Charnock et al. 1994, Howarth et al. 1994). She reported that using a quasi-equilibrium MYG “k” model was an improvement over Sharples & Tett (1994) who used MY level 2. Li & Elliott (1993) also had apparently used an MY level 2 but had to multiply the wind stress by 3.3 to produce the same mixed layer depth as in the observations. This multiplication was not necessary using the MYG level 2.5 scheme. Chen & Annan (2000) used the Sharples & Tett (1994) physical model, the Warrach (1998) temperature model, and the biology model of Tett & Walne (1995) to form a 1-D model representing the point CS. Of the range of turbulence closure schemes they found that the MY level 2 gave poor results producing a thermocline that was too shallow, the other three methods, MYG 2.5 k model explicit, MYG level 2.5 k implicit and the one equation k-l implicit method gave comparable results. They suggested that turbulence schemes should be tested by biological modelling. Vösumaa & Heinloo (1996) proposed models for the evolution of the vertical structure of the turbulent active layer of the sea taking account of the speciﬁc role of the large-scale turbulent eddies in the mixing process. They reported a 1-D differential model of vertical structure based on the theory of rotationally anisotropic turbulence (RAT) which can provide a computationally cheaper averaged description of effects connected with the eddy-like structure of turbulence. Several sub-models were proposed. It is interesting that the 1-D General Ocean Turbulence Model GOTM, which has its origins in the PhD thesis of Burchard (1995) and is now a community model (Burchard et al. 1999), will act as a testbed for the intercomparison of schemes associated with turbulence modelling.

Simpliﬁcation Some turbulence schemes can be computationally expensive, and so it is of interest to ﬁnd situations and/or methods whereby simpler forms can be used without signiﬁcant loss of accuracy. For example, Xing & Davies (1996b) presented the application of a range of turbulence models. They cautioned about the correct speciﬁcation of the mixing length in some schemes but found that in the applications considered, a simple ﬂow dependent eddy viscosity with parabolic variation of viscosity performed as well as more complex schemes. Davies & Hall (2000) examined the spatial variability of the M2, S2, N2, K1 and O1 tidal constituents, in the North Channel of the Irish Sea and there appeared to be very little difference between a turbulence closure method and ﬂow-related eddy viscosity. The cycle of tidal turbulence closely correlated with tidal current and water depth may be the reason for this occurrence. Berntsen & Svendsen (1999) presented a comparison between BOM and POM against observations. Tests were run with BOM using a higher order scheme and a simple Richardson number method. In this particular instance it was uncertain whether the higher order closure improved results. Verdier-Bonnet et al. (1999) decided to use the simpler k-ε closure for a coastal upwelling system. However this scheme assumed eddy viscosity isotropy so to take into account the non-isotropic turbulence due to buoyancy and Coriolis, a hybrid turbulence model 77

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STRAT-COR that takes into account stratiﬁcation and Coriolis forces was presented. A further turbulence model ASPECT was also presented for shallow water, which added a horizontal diffusivity contribution to the standard model. The authors cautioned about anisotropic effects that could be particularly signiﬁcant in regions of freshwater inﬂuence. The GHER turbulence closure scheme was presented by Delhez et al. (1999) and two applications of the model, the shallow tide dominated northwest European shelf and the deeper Mediterranean, were given. The dominant turbulence terms from theory and from simulations were examined and in both cases there was dominant production and destruction; advection and horizontal diffusion were not so important hence simpler turbulence models were possible. It is concluded that the most general model should be used, a diagnosis performed, and then the model could be simpliﬁed if possible. Further papers present methods of representing turbulence. Hoch & Garreau (1998) presented a computationally inexpensive scheme for the rapid modelling of phytoplankton. It consisted of residuals derived from an existing hydrodynamic model coupled with a 10-level vertical thermal model. The vertical turbulence was a Prandtl mixing length scheme where the length is related to the Richardson number. In the paper by Giménez-Curto & Corniero Lera (1996) turbulent ﬂows over very rough surfaces were examined. They commented that the shear stress may be modelled by either a simple eddy viscosity or a reﬁned turbulence scheme but in the case presented, the simpler eddy viscosity seemed to give better results even for cases with very large roughness. In Bijvelds et al. (1999) an idealised test laboratory model was compared with the TRISULA numerical model. The ﬂow in the test case simulated a river discharge. Due to the non-isotropic ﬂow an alternative two-length scale turbulence model splitting the bottom generated turbulence and that due to horizontal shear was proposed. This effectively added a depth averaged k-ε and it improved the representation of the ﬂow. Finally, Croft et al. (1996) announced a new type of second-moment closure to satisfy the two-component limit to which turbulence reduces at a wall or sharp density interface. This was adopted for the prediction of buoyancy-affected ﬂows and several examples were given.

The behaviour of turbulence Apart from the comparison of turbulence schemes and simpliﬁcations that can be applied, it is of interest to note the time and space scales of variation of turbulence itself. Pohlmann (1996b), using the modiﬁed Backhaus (1985, 1989) model of Pohlmann (1996a), showed that eddy viscosity goes through a seasonal cycle in the North Sea. Sharples & Simpson (1995) using a 1-D model with level two turbulence closure, reproduced observations of density driven ﬂow pulses that occur at slack tide due to reduced turbulence. They also showed how at neap tides the ﬂows follow isopycnals more closely. For a more idealised case of a rectangle representing the North Sea, Davies & Jones (1996) illustrated the inﬂuence of wind and wave turbulence upon tidal currents using a ﬂow-related eddy viscosity scheme. As this was a very idealised case it highlighted, for example, the need in models for an additional background turbulence which may not at ﬁrst be apparent in more complex simulations. It must be emphasised that such idealised models are very useful indeed in highlighting the speciﬁc contribution of various processes such as bottom friction (e.g. Carbajal 1997). This point is raised in the discussion at the end of this review.

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Boundaries Having examined the main hydrodynamic features of coastal and shelf-sea numerical models, their boundary conditions, constraints and included processes can now be explored. To a certain extent the responses to the questionnaire become somewhat less relevant as many features are now optional. For example, not all models include the prescription of wind stress. However, within each option areas of consensus or disagreement can be sought.

Surface boundaries The surface boundary of the sea is subject to wind stress, pressure gradients, thermohaline and other ﬂuxes, and is subject to disturbance by waves and may be affected by the presence of ice. There are a number of standard methods for prescribing surface conditions and the results from the survey indicated that a variety of these were in common use.

Wind stress, pressure gradients, thermohaline ﬂuxes The wind stress imposed on the sea surface may come from a simple wind stress (usually quadratic) law. Traditionally, models simply related surface wind stress to wind speed with a drag coefﬁcient dependent on the wind speed (e.g. Smith & Banke 1975, Smith 1980, Large & Pond 1981, Luthardt & Hasse 1983 or others) and these schemes were all used by models reported in the survey. More recently, the changes of sea surface roughness due to wind waves, which can change the drag coefﬁcient, have been included. This discussion is therefore included in the section on waves (see p. 101). For surface heat ﬂuxes two types of surface boundary condition are usually suggested (Pohlmann 1996a). The ﬁrst is to apply heat ﬂuxes through the surface (von Neumann boundary conditions), which requires a knowledge of various parameters such as direct solar input as modiﬁed by cloudiness, latent heat ﬂux, sensible heat ﬂux and long wave radiation ﬂux but this could be a problem if any of these data are sparse or unavailable. For example, in Roussenov et al. (1995) where a MOM model of the Mediterranean was demonstrated and a 70-year simulation performed, although most of these parameters could be calculated from basic meteorological data, a value for cloudiness had to be assumed. An alternative approach may be a Dirichlet boundary condition which imposes sea surface temperature directly and which may be more readily available. A further surface condition, the surface salinity ﬂux, is in practice simply speciﬁed as the local difference between evaporation and precipitation if these parameters are known. Wind stress can induce ﬂows that cause mixing in the upper layer of the sea, and this stress is featured in many models. Broström & Rodhe (1996) presented a study of a winddriven turbulent Ekman layer in the presence of a horizontal density gradient. A 1-D mixing k-ε model is solved using the PROBE (Svensson 1998) equation solver and one conclusion was that models of areas where strong horizontal density gradient exist, must have high resolution in the vertical near the surface to take proper account of shear and hence mixing. Mention has already been made of the study using an analytical model and a numerical model by Luyten (1996) for examining the evolution of a wind driven surface layer.

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Langenberg et al. (1999) used two models: a statistical down-scaling model and a 2-D dynamical storm surge model incorporating features from HAMSOM, to study the effect of a changing wind climate on storm surges on the northwest European continental shelf. The numerical model examined the years 1955–93 and the statistical model examined the years 1899–1993 to establish recent variability and then two runs from an atmospheric model, one with doubled carbon dioxide were used as input. This study is comparable to an investigation by Flather & Smith (1998) who use the CSX model, a 35-km resolution 2-D storm surge model (which was the basis of the ﬁrst operational surge forecasting scheme in the UK). This surge model uses the same two climatic scenarios. In both models the effect of doubling carbon dioxide is only marginally seen above present day variability. Pierini & Simioli (1998) demonstrated a free surface barotropic primitive equation model for the whole of the Mediterranean and performed a sensitivity to wind stress analysis. This model tested the view that the main circulation of the Tyrrhenian Sea, which is a gyre, is primarily wind-driven. Myers et al. (1998) assessed the impact of three different wind climatologies on the circulation of the Mediterranean using the MOMA model. It was shown that different climatologies can signiﬁcantly affect the thermohaline circulation and that the choice of wind may depend on the application in mind. In a subsequent study, Myers & Haines (2000) re-examined the surface ﬂuxes by carrying out a 100-yr integration with the surface restoring to monthly varying temperature and salinity. From the last 15 yr of the run the surface ﬂuxes of heat and fresh water were diagnosed in monthly averages and applied in a further 100 yr run with these ﬂuxes only. The model remained stable throughout this second run although there was more variability. The same model was used by Samuel et al. (1999) using two-monthly climatologies for the years 1980–87 and 1988–93. These simulations were used to show that a signiﬁcant change in deep water production in the eastern Mediterranean has been induced by changes in winter wind stress. However the frequency of the wind input can also be signiﬁcant. The 1-D model of Warrach (1998) conﬁrms that higher frequency input causes higher ﬂuctuations and hence a deeper thermocline. In the same paper it is reported that monthly averaging of the solar radiation input can lead to excessive heating. Another example is Stanev et al. (1995) who found, when using the GFDL model in a general circulation model of the Black Sea, that frequency of wind and other atmospheric inputs is important not only for short period events but long-term formation of water masses. Staneva et al. (1995), continuing this study, commented that monthly average inputs are insufﬁcient. In a further study, Staneva & Stanev (1998), employing a MOM based model, used different climatic datasets for the Black Sea, and included the Bosporus inﬂow and the Mediterranean plume. In addition they used a new set of wind stresses based on ship observations. Five different scenarios with different combinations of heat ﬂux, freshwater input and wind stress were tested and it was found that the new set of wind stresses from observations were a deﬁnite improvement. The Black Sea was also modelled by Oguz et al. (1995) using a version of POM, including orthogonal curvilinear representation. The effects of different wind ﬁelds were tested. An annual mean wind stress did produce the main gyres seen in the Black Sea but use of an annual mean heat ﬂux produced unrealistic patterns. This study infers that seasonal variation in the winds is relatively less important than seasonal heat ﬂux variation for the main features seen in the Black Sea. The same may be inferred for the Mediterranean. Castellari et al. (1998) examined the most commonly used heat ﬂux bulk formulae in connection with a model of the Mediterranean using the MOM based MEDMOM model of Roussenov et al. (1995) and Pinardi et al. 80

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(1997). They produced a 12-hourly calibrated dataset that agreed with the long-term net negative surface heat ﬂux balance required for the Mediterranean. On input to the model the water mass representation was remarkably improved. This was followed up by Castellari et al. (2000) where the model response to a 12-hour and monthly forcing was tested. The sensitivity of several water mass types to the different forcings was made apparent and monthly forcing was unable to produce certain water types. Drakopoulos & Lascaratos (1999), who used a POM based model of the Mediterranean, were able to reproduce general circulation features using monthly winds but were unable to form certain deep water types. They suggested using daily forcing.

Freshwater, salinity ﬂuxes The treatment of rainfall or evaporation or the effective balance between them in coastal models may be very signiﬁcant. Unfortunately, measurements of these two parameters at sea are rather sparse. Indeed, Pohlmann (1996a) commented on the lack of evaporation and precipitation data. In an early study using an advection–diffusion model and comparing it with observations, Jones & Howarth (1995) reported the inferred evaporation/precipitation balance of the southern North Sea and compared it with the wide range of estimates that had been reported from various sources. Roullet & Madec (2000) used a global version of the OPA model to examine the freshwater ﬂux formulation in ocean general circulation models from the point of view of salt conservation. Four formulations are presented: virtual salt ﬂux, natural, linear free surface and assumption free. The linear free surface scheme emerged as the best compromise in this study. The correct input of rain may have other signiﬁcance. Moore et al. (1998) reported that the ﬂux of nitrate from rain into a river plume was in fact twice that from the river itself.

Sea ice In the treatment of sea ice many numerical models take the Hibler (1979) viewpoint where ice, although granular, is treated as a continuum. Ice is assumed to be a 2-D isotropic compressible ﬂuid with a viscous-plastic behaviour. Ice is subject not only to dynamical behaviour such as deformation and drifting but also thermodynamic considerations such as melting or growth. These two processes imply release or absorption of latent heat hence a discontinuity in heat ﬂux exists at the sea surface. There is also a matter of different time and space scales. Mechanical properties can affect the entire ice sheet whereas the thermodynamics may be a local effect. An overview of these considerations is given by Kleine & Sklyar (1995) and Leppäranta (1998). The viscous-plastic (VP) rheology apparently has problems in responding to rapidly changing forcing ﬁelds. Hunke & Dunkowicz (1997) therefore developed an elastic-viscous-plastic (EVP) model as a way of tackling the problem. Apparently it also has advantages for parallel computer architecture as used, for example, in the SWECLIM project (Meier et al. 1999). Other modelling schemes are available. Schrum & Backhaus (1999), for example, used a 3D baroclinic coupled ice-ocean model based on HAMSOM coupled with a modiﬁed Hibler-type model. In this case the dynamics come from Leppäranta (1981) with the viscous-plastic rheology from Leppäranta & Zhang (1992, unpubl. report). Another sea-ice model which has been used in the European context is that of Parkinson & Washington (1979). In this model the thermodynamics come from the energy balances and the dynamics arise from ﬁve basic stesses, wind, water, Coriolis, 81

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internal ice resistance and tilt of the sea surface. Quite often (e.g. Harms et al. 2000) elements of this model as well as of the Hibler (1979) model may be used in conjunction. Mention must be made of a sea-ice model intercomparison project SIMIP, which involves partners from within and outside Europe (Table 2, p. 50). Using modelling studies to investigate the dynamical properties of the ice, Uotila (2001) using a Hibler model, noted that the ice drift is related to wind drift in the open sea but this relation breaks down in coastal regions. Using ﬁve types of wind stress and two types of current an air-ice drag coefﬁcient was estimated. Saloranta (2000) demonstrated a 1-D ﬁne grid thermodynamic model of snow, snow ice and ice in the Baltic Sea that took into account such factors as slush refreezing and sensitivity to precipitation. Steiner et al. (1999) included sea-ice roughness as a prognostic variable and discussed the impact on wind drag coefﬁcients. Schrum & Backhaus (1999) examined the sensitivity of the seasonal cycle to forcing changes on a seasonal cycle. An interesting deduction was that sea-surface temperature variability is not always a measure for heat content variability in the North Sea and Baltic. Omstedt et al. (1996) reported the ice-ocean response due to variable winds in two models, the ﬁrst analytic, the second numeric with a two equation turbulence scheme. One aspect of this investigation was that it showed the maximum ice and current responses when the wind variability was near the inertial frequency. As mentioned above, the 13 subbasin model of Omstedt & Nyberg (1996) examined the response of Baltic to seasonal, interannual and climate change forcing and concluded that the Baltic is particularly sensitive to climate change. The thermohaline variability of the Baltic was examined by Lehmann & Hinrichsen (2000) by means of a coupled ice-ocean model using the Hibler approach coupled with a Bryan–Cox–Semtner model with Killworth et al. (1991) modiﬁcations. They conﬁrmed the point of view that ﬂuctuations of meteorological input have a strong inﬂuence on the Baltic. Haapala & Leppäranta (1996) used an ice model based on a simpliﬁed form of the Hibler model which included three levels of ice cover: open, undeformed and ridged. In a much more restricted space scale Kämpf & Backhaus (1998) reported a 3-D nonhydrostatic convection model that accounts for small-scale ice-ocean interactions and was used to study convection in shallow seas (coastal) ice formation regions. A comparison with a 2-D slice model showed that this model exhibited the main features of the circulation found in the 3D model. Harms (1997) used the HAMSOM model to investigate potential leakage from dumped radionucleides in the Kara Sea. The model was coupled with a thermodynamic and dynamic sea-ice model based on Hibler. Several leakage scenarios were tested. A mechanism was mentioned where bottom sediments are stirred up and brought into contact with ice formation. This effect is interesting when associated with the mechanism described in Sherwood (2000) where ice may raft contaminants a greater distance with therefore less effective dispersion than if ice were absent. Another mechanism associated with sea ice is described by Fennel & Johannessen (1998) whose analytical study examined the effects of ice-edges, which can cause local up- or downwelling due to wind. Polyokov (1999) presented a hydrodynamic z-co-ordinate model of the Arctic Ocean coupled with an ice model that has elastic-plastic behaviour for ice internal stresses, and a distribution function for ice thickness and consolidation. Six distinct categories of ice thickness were simulated. The model has a special algorithm for advection and ridging, and polynyas and leads appear in the model. Hilmer et al. (1998) used an ice model based on Harder et al. (1998) to simulate variability of sea-ice transport between the Arctic and North 82

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Atlantic that could affect climate. The model is an optimised dynamic–thermodynamic model that carries out a 40-year integration. A relation was inferred between atmospheric pressure anomalies and ice export.

Other surface inputs While noting surface conditions for models it is worth mentioning that ﬂuxes of other substances may be imposed and included in the model circulation. For example, Stratford et al. (1998) used the transports from Haines & Wu (1998) to look at oxygen ages in different parts of the Mediterranean. The model included air–sea transfer, entrainment and oxygen consumption. The oxygen ages produced by the model showed that the western Mediterranean basin was better ventilated than the eastern basin.

Boundary condition at sea bed From hydrodynamical aspects, the seabed boundary is quite a complex system with high shears, due to friction, generating turbulence and a bottom boundary layer with a velocity proﬁle that is usually logarithmic in form. In shallow water additional seabed turbulence may result from wave action, and in shallow regions the bottom boundary layer may reach the surface. Luyten (1996) discussed the evolution of a bottom boundary layer. However, in many models the seabed condition may be simply represented by a friction law, often quadratic, related to the nearbed ﬂow or in 2-D models to the depth mean ﬂow. The response to the survey showed that in those models that required a seabed condition, about 80% of respondents used a quadratic stress condition so there was a measure of consensus. In most cases a form of slip condition was used at the sea bed but a no-slip condition can be used. Xing & Davies (1996c) compared a model using a slip condition with the no-slip model of Xing & Davies (1996e). In such models where grid boxes are used in the vertical, a ﬁne resolution must be used near the sea bed. A similar comparison between a slip and no-slip condition using a functional model is presented by Davies (1993) where arbitrarily high resolution may be used. However, as Davies et al. (1997a) point out there are computational advantages to a slip condition in being able to separate the barotropic and baroclinic modes that contribute to the vertical current proﬁle. It is interesting to compare the results from Davies & Jones (1993) where bed stresses are determined in three ways: from depth-mean current in a 2-D model, from the nearbed current in a full 3-D model, and from a 2-D model using a deconvolution method to extract nearbed currents. The 3-D model is clearly superior only in very shallow water, which is a similar conclusion to that made in Prandle (1997) for tidal ﬂows. In the latter paper a 1-D model in the vertical is used, and comment is made that a 2-D depth integrated model may be sufﬁcient to calculate bed friction from depth-mean ﬂow. In shallow water, however, a 3-D description may be required. He incidentally notes that at the critical latitudes where inertial frequencies are close to the diurnal or semi-diurnal tidal frequencies there is enhanced sensitivity to frictional effects. The standard quadratic friction coefﬁcient C100 is usually related to the velocity 1 m above the sea bed. For a given bed roughness the coefﬁcient is often assumed constant but Green & McCave (1995), from an analysis of observations in the eastern Irish Sea, found that it can be highly time variable and suggested a classiﬁcation scheme depending on the type of ﬂow. 83

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Sometimes the question of bed friction is taken in isolation. An inter-relation between internal turbulence and bed friction is indicated from tests with 1-D, 2-D and 3-D models (Lee & Davies 1999). For each viscosity parameterisation there may be an optimal bed friction coefﬁcient. The coefﬁcient of bed friction may, therefore, not be an independent variable.

Boundary condition at the coast From the survey it was unanimous that in those models that required a condition at the coast that a condition of no orthogonal ﬂow should be speciﬁed. However, as at the sea bed, there was a choice of slip, partial slip or no slip conditions. In most cases a full slip condition was used but a few other types were reported. In specifying the condition along the coast the horizontal discretisation of the model is signiﬁcant, particularly where the use of a regular rectangular grid causes stepped coastline representation. Even in the presence of a straight coastline, spurious ﬂows may be generated due to the averaging involved in ﬁnite difference schemes (Jamart & Ozer 1986). In an idealised study, Adcroft & Marshall (1998) examined the effect of the orientation of a ﬁnite difference grid and showed that a form stress can result depending on the implementation of a slip condition and the type of the viscous stress tensor. In fact, a free-slip condition can be reduced to no-slip by this effect. To avoid this behaviour they suggested writing the viscous stress tensor in terms of vorticity and divergence. They also brieﬂy touched on the use of ﬁnite elements or shaved cells. Matthews et al. (1996) also considered the problem of representation of the coastline and in this case used a series of oblique piecewise segments which were combined with a slip condition. Signiﬁcantly, improved results were demonstrated. Deleersnijder (1996) presented a detailed study of different ways of treating the coastal boundary layer. Arakawa B and C grids with slip or no-slip conditions were compared with an analytic solution. The four main conclusions of this study related to such items as grid resolution of the boundary layer but with some qualiﬁcation the use of a slip condition was preferred, the C grid having a slight advantage.

Wetting and drying Finite element The deﬁnition of wetting and drying usually refers to the masking out of grid boxes or partial grid boxes in a model depending on the level of the water therein. From the results of the survey the implementation of a wetting and drying scheme seemed to be rather infrequent but this presumably was only used in models of the immediate coastal zone. Wetting and drying can have an effect: for example, in the use of the ADCIRC ﬁnite element model by Fortunato et al. (1999) the inﬂuence of wetting and drying on the tidal dynamics in an estuary were assessed. Other ﬁnite element models that include wetting and drying are reported by Heniche et al. (2000) and it is also incorporated in the TELEMAC system. The paper by Ip et al. (1998) investigated a ﬁnite element wetting and drying scheme in a more idealised situation. Finite difference In ﬁnite difference schemes Bolzano (1998) reviewed 10 different methods. Another detailed study of wetting and drying schemes was given by Flather & Hubbert (1990). George & Stripling (1995) develop a method of sloping facets as well as reviewing 84

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two other schemes. For continuity reasons, Schönfeldt (1997) took into consideration the water deﬁcit due to a wetting and drying scheme. The depth-integrated DIVAST model of Lin & Falconer (1997) was modiﬁed to provide a 3-dimensional layered model to tackle the problems of ﬂooding and drying in estuaries. Flooding and drying of the Wadden Sea also forms part of a large-scale study of oscillations in the Baltic using satellite, modelling and tide gauge data, the BSH model being used in this case (Metzner et al. 2000). Özkan-Haller & Kirby (1997), using shallow water equations in the nearshore environment, considered the treatment of a moving shoreline and reviewed the topic. Although a non-European paper, it is worth mentioning the paper by Shi et al. (1997) which considered not only water level rise but the ﬂooding that occurs during storm surges. This approach suggests that future storm surge models should incorporate a representation of inundation as well. Thus the land/sea interface could become less deﬁnite. An inundation model drawing on Australian experience with an assessment of planning requirements is that of Hubbert & McInnes (1999). The ﬁnite element inundation model for a Mediterranean area, as presented by Tucciarelli & Termini (2000), may have the elements of a future combined surge and inundation model. Mention must also be made in this section of digital elevation models that employ hydrodynamic models as well as satellite imagery to build up an intertidal model (Mason et al. 1997, 1998, 1999). Such models can be used to measure sediment loss or gain and can estimate transport at reasonable cost. In a 3-D functional model, Davies & Aldridge (1993) found that there were problems with bottom friction in shallow water as water depths became very shallow. However, by modifying the numerical algorithm, a stable method was obtained and successfully used in a 3-D storm simulation taking account of wave current interaction (Jones & Davies 1998).

River inputs The freshwater river inputs into a model naturally have an effect on the local salinity. There may also be thermal effects, the river may impart momentum and it may carry biochemical substances as well as sediments. They can also produce plumes which may affect model circulation. From the questionnaire it appeared that rivers were most commonly represented by a simple discharge of fresh water of zero salinity at local temperature imparting no momentum. In certain models such as the TELEMAC system, the POL3DB model, OSMOM, the NZB, the FVM, the Catalan continental shelf high resolution model and the GHER model, the temperature of the river may be speciﬁed. Momentum is also included in HYCOM, HAMSOM, OSMOM and it is an option in the FVM and NZB models. Various chemical substances, some of biological signiﬁcance, may be discharged. The DYMONIS advection–diffusion model examines the spread of nitrates from rivers surrounding the Irish Sea. A simple 2-D advection–diffusion model (Jones & Howarth 1995) was used to assess the budgetary effect of river freshwater ﬂow compared with freshwater input due to precipitation in the southern North Sea. The river ﬂows into a model can be signiﬁcant, particularly in limited areas and can be a major concern if the rivers are carrying pollutants. The plumes or regions of freshwater inﬂuence (ROFI) that are produced by rivers was a concern of the European PROFILE project (Huthnance 1997a,b). One aspect of the modelling effort was the emergence of the MU-ROFI model (Ruddick 1995, Ruddick et al. 1995), which was based on the framework proposed by Wolf (1991). In due course, with contributions from other European partners, this developed into the COHERENS model (Luyten 1999). Xing & Davies (1999a) examined the effects of river input and wind upon 85

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the along-shore propagation of the Ebro river plume, which is in an area of very small tidal activity. They found that besides wind effects advecting the surface plume it increased vertical mixing. They also showed the sensitivity of plume dynamics to the choice of turbulence scheme. Hearn et al. (1999) used the Stommel (1961) thermohaline convection model to examine estuaries under drought conditions. From this they developed a method of classifying estuaries into four types. Estournel et al. (1997) used the Johns et al. (1992) model to study the behaviour of the Rhône plume under wind forcing. In an idealised study, using the POL3DB Proctor & James (1996) model, James (1997) showed how a plume can turn in an unexpected direction due to the presence of a gulf of limited area. De Kok (1996) used a two-layer model of the Rhine plume in which the grid moves with the vertical position of the density interface. It was used to demonstrate the inﬂuence of effects, such as wind, on the patterns of salinity and the residual ﬂows. Langenberg (1997) used the Pohlmann (1996a) model to study the effect of wind on the Rhine plume. Luyten (1997) used the Rhine plume as a case study, where an analytic 1-D two-layer model examined haline stratiﬁcation which was then compared with a numerical point model with advanced turbulence closure. Souza & Simpson (1997) in a reduced physics slice model examined the effect on water column stability due to tidal straining and stirring. Marsaleix et al. (1998) used the 3-D model of Pinazo et al. (1996), itself based on the Johns et al. (1992) model, to investigate the plume dynamics of the Rhône. They showed the importance of JEBAR (the Joint Effect of Baroclinicity and Bottom Relief ) and nonlinear terms. They also reported that a double σ-co-ordinate scheme gave better results. It should be mentioned that the JEBAR effect is a matter of discussion (Cane et al. 1998). This effect was originally described by Sarkisyan & Ivanov (1971) and may be relevant in certain situations (Pierini & Simioli 1998). Dippner (1998) examined the processes at the plume river front of the Elbe with an eddy resolving model containing a vorticity equation that shows the effects on the eddies of wind stress interacting with the gradient of the bottom topography. The Oguz et al. (1995) model of the Black Sea circulation, which is based on the POM model with orthogonal coordinates, includes river runoff. In this case, however, there is also the complication of the exchanges through the Strait of Bosporus. Simpson & Souza (1995), using a 1-D model forced by observed slopes and local density gradients, examined a group of four processes in the region of the Rhine ROFI including tidal straining. Langenberg (1998) investigated coastal currents forced by buoyancy discharges and how they can become unstable. The German Bight and the Skaggerak were compared in terms of kinetic energy, baroclinic and barotropic energy conversion and wind. Kourafalou (1999) using a version of the POM model as modiﬁed in Kourafalou et al. (1996) carried out experiments representing the Po river plume in the northern Adriatic, to examine bouyancy driven ﬂow and interaction with topography, wind stress and ambient stratiﬁcation and the implications for biology. They showed that fresh water can inﬂuence circulation over an entire basin and not just in a local shallow region. An interesting complication in river ﬂows is the presence of ice. Harms et al. (2000) examined the fate of discharges from Siberian rivers into the Arctic Ocean using the HAMSOM model coupled with a sea-ice model based on Hibler (1979) and Parkinson & Washington (1979). The model showed the spatial distribution of contaminants and emphasised that, because of the strong seasonality, the fate of the contaminants depends strongly on the time of discharge. 86

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Open boundaries From the responses to the survey, the treatment of open boundaries was undoubtedly an area where there was no consensus. Perhaps this is not surprising as many schemes exist and the type of model and the location of the boundary can inﬂuence the method used. Traditionally in shelf-sea dynamics, an elevation and/or velocity speciﬁed method or a radiation condition can be used at the open boundary. In certain cases, as we have seen, the information may come from an exterior model. The open boundaries of a hydrodynamic model can be crucial to the entire solution within the model domain. This is particularly so for a limited area model. For example, the FREISM model (Jones & Davies 1998) highlights the importance of the correct imposition of surge elevations and currents from an exterior model. In Androsov et al. (1995) the open boundary conditions become part of a boundary value problem in a curvilinear grid representation of the Strait of Messina. Other boundary conditions were reported in the survey. For tracers a relaxation scheme was used by a few modellers. Another approach was the use of a sponge layer. Other reported open boundary methods were the zero gradient condition and the Thompson (1990) condition (used with TELEMAC). Naturally, the way to avoid an open boundary is to use a periodic condition or a global model. As coastal models spread further into deeper water and global models, particularly ﬁnite element models, acquire even ﬁner grid resolution, such a possibility may not be too distant (Lefèvre et al. 2000). An early overview of barotropic boundary conditions is given by Chapman (1985). More recently the paper by Palma & Matano (2000) which, although not concerned with a European application, gives an overview of 13 types of open boundary conditions for barotropic and baroclinic motion, tracers and composite schemes. In a series of tests based on the POM model the composite scheme MOA gave the best overall performance. However other schemes were useful in particular situations. In a limited area model such as the FREISM eastern Irish Sea model (Jones & Davies 1998), prescribed boundary currents and elevations from an exterior model make a very signiﬁcant contribution to the circulation in the interior model even with the use of a radiation condition. Another overview of open boundary conditions is presented by Jensen (1998). The various types – clamped, prescribed, zero-gradient, Camerleng-O’Brien, Orlanski, a method of characteristics following Thompson (1990), and the ﬂow-relaxation scheme (FRS) which is a type of sponge – are implemented in the threelayer isopycnal JENSEN model. A number of different scenarios are tested and the FRS and method of characteristics appear to give the overall best results but simpler models give good results in certain cases. By contrast, Penduff et al. (2000) using a SPEM 5.1 model of the North East Atlantic, where the open boundaries are in deep water, allowed the open boundary barotropic ﬂows to be determined entirely by the internal model dynamics. The model remained stable and quite realistic. A very recent, non-European paper (Marchesiello et al. 2001), also gives a review of open boundary methods and then discusses an adaptive form of open boundary condition. On the shelf in shallow water there is a high friction regime. In these regions open boundary conditions can be quite robust. The relatively high dissipation tends to smooth out small errors in input but the imposition of open boundary conditions on the slope or in deep water may be more difﬁcult to implement. Lam (1999) reported observed diurnal shelf waves at the Greenland shelf edge and developed a simple 2-dimensional model to illustrate their spatial distribution including the shelf. These slope processes could be important in shelf model boundary conditions. Xing & Davies (2001a) using a 3-D model of the 87

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Malin Shelf demonstrated how shelf edge ﬂows can leak onto the shelf. This further emphasises the importance of studies such as those by Huthnance (1975, 1978) in modelling these processes because they may in future be a necessary requirement for open boundary speciﬁcation. The application of deep-water boundary information of ﬂows, temperature and salinity from ocean models at the boundaries of shelf models is not a simple task. As mentioned earlier, the lack of friction in deep regions means that any inaccuracies in the ﬂow presented to the inner model will persist and work is proceeding on the most suitable methods of avoiding these. For example, the POLCOMS system incoporates a model of the shelf and adjacent deep ocean that accepts boundary information from the Met Ofﬁce (UK) FOAM model (Bell et al. 2000). This system has already become operational. It is also an aim of the EU-GANES project to apply model nesting techniques with boundaries that could lie in these deeper regions.

Included processes Temperature and salinity From the responses to the questionnaire it seems as if the day of the purely hydrodynamic model (which assumes water of uniform density) is over. Approximately 75% of the models surveyed included modelling of temperature and salinity in diagnostic or prognostic form or had an option to do so. Temperature and salinity are obviously important for density and stratiﬁcation aspects but methods can vary in describing them. Pohlmann (1996a) commented on the disadvantages of diagnostic modelling where ﬂows may be suppressed by the “frozen” temperature and salinity ﬁelds or, if the ﬁelds are varied from time to time, then there could be jumps each time new ﬁelds are imposed. Pohlmann (1996a) presented a model based on HAMSOM for the northwest European shelf, which is prognostic for temperature and in this case diagnostic for salinity because salinity is perhaps only important in the Norwegian Coastal Current and near coasts. A Dirichlet boundary condition is used imposing sea surface temperature directly. This is applied weekly and the seasonal evolution of the thermocline and its interannual variation is simulated (Pohlmann 1996c). One feature that appears from the model simulation is that in midsummer the fronts in the North Sea seem to be dependent on tidal stirring rather than thermodynamic inputs. In a further investigation of the heat content of the North Sea (Pohlmann 1996d) it appears from a 10-yr simulation that during the 1980s there was a distinct switch from one form of heat regime in the North Sea to another. Schrum (1997), using the same model system with a few modiﬁcations to the turbulence scheme, looked at processes inﬂuencing the development and mesoscale variability of thermal stratiﬁcation in the German Bight. In this case, prognostic salinity was also included as temperature and salinity are prescribed at the boundaries of the model and also a salinity condition exists at river mouths. It is a ﬁne resolution model and hence eddy resolving. The sensitivity of the development and mesoscale variability of the thermocline to wind forcing and other parameters is examined. Baroclinic instability is identiﬁed as a process responsible for thermocline stratiﬁcation in the German Bight. The role of such eddies is important and 88

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steps have to be taken to include their effects if possible where mixing is concerned. For example, Wu & Haines (1996) and Haines & Wu (1998) present a general circulation model of the Mediterranean based on the MOM 1.0 model. It was shown that baroclinic eddies are critical to the dispersal of Levantine Intermediate Water (LIW). In the latter paper, using a mean annual cycle of atmospheric forcing with relaxation to monthly mean surface temperature and salinity, a series of experiments were performed in which the viscous coefﬁcients were varied either to suppress the formation of eddies or to allow larger scale eddies to form. The model grid spacing of 1/4 degree does not allow full eddy resolution but this is parameterised in a further experiment using the scheme of Gent & McWilliams (1990); this latter scheme showed the most promising results. The model of Wu & Haines (1996) was subsequently used by Stratford & Williams (1997) to examine the formation and dispersal of LIW treating it as a passive tracer. In the model of Holt & James (1999a) a fully prognostic temperature (no salinity included) simulation was performed using a 22-km resolution grid based on the Proctor & James (1996) model, POL3DB. It used an MY 2.5 turbulence closure, PPM advection and bulk heat formulae were applied at the sea surface. A stable seasonal cycle was achieved after a simulated 4 yr. Becker et al. (1997) compared a 39-yr simulation using the HAMSOM model for transports with observations of temperature and salinity using trend analyses, cross-correlation and power spectra. This model deduced periodicities and related the circulation on the northwest European shelf to the Atlantic circulation, the North Sea being apparently inﬂuenced by the subpolar gyre whereas the Bay of Biscay is inﬂuenced more by the Atlantic subtropical gyre. The paper by Jones & Howarth (1995), using an 8-km grid resolution advection–diffusion model of the salinity in the southern North Sea, infers Atlantic inﬂuence as the source of slightly fresher conditions seen in the eastern North Sea during the NERC North Sea Project period 1988–89. Incidentally, this paper also infers the precipitationevaporation balance over the North Sea by carrying out a sensitivity study of salinity patterns compared with observations. Schmidt et al. (1998) modelled patterns of salt propagation in the southwestern Baltic Sea using a GFDL based system with a Killworth et al. (1991) free surface formulation. A telescoping grid (Fig. 3) was used to include far-ﬁeld effects with a Richardson number parameterisation for vertical mixing and constant horizontal turbulent viscosity and diffusivity. The results were compared with ﬁeld measurements and good agreement was reached provided the output of the model was synchronised with the timing of the sections worked by the ship. The previously mentioned 13 sub-basin model of Omstedt & Nyberg (1996) and Omstedt & Axell (1998) demonstrates an ability to simulate the variations in temperature and salinity in the Baltic. The bulk ﬂow of freshwater runoff is transmitted through the model and there are special conditions for sea ice. Parameterisation of frontal mixing and movement in the Kattegat is required. It took 10 yr to spin up the stratiﬁcation in the Kattegat but 100 yr in the Baltic proper. Marsh (2000) discussed the representation of cabbeling. This is the process of density gain through the isopycnal mixing of water parcels with the same densities but different temperatures and salinity. It can be an important source of mixing, so if a model such as MICOM is run with salinity only, then cabbeling cannot occur and the model is therefore unrealistic. In a demonstration of modelling cabbeling, an overprediction occurs in the region of the Mediterranean outﬂow due to the initial ﬁelds used. 89

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Langenberg (1997) used a version of HAMSOM similar to Pohlmann (1996a) to examine the ROFIs of the North Sea. The model is fully prognostic for salinity containing advection– diffusion with sources and sinks. The diffusivity in this case is derived from the eddy viscosity by way of a turbulent Prandtl–Schmidt number related to the Richardson number. At estuary mouths a scheme is used where a ﬁlling and emptying freshwater box is used to allow for the tidally induced pulsing seen in a typical estuary. Three models, barotropic, baroclinic and a coarse resolution version are used, which demonstrates that ROFIs would be ignored by a 20-km resolution model. Nittis & Lascaratos (1998) used a hierarchy of three diagnostic and prognostic models and three types of forcing to calculate LIW water mass transformation in the eastern Mediterranean. A semi-analytic diagnostic model was compared with a 1-D mixed layer prognostic model, which was implemented at different locations. A 3-D POM based prognostic model of the area was also used. Jungclaus et al. (1995) presented a model that considers the formation of dense water due to brine enrichment under ice. In a fjord situation in Svalbard the dense water may overtop the sill at the entrance of the fjord. A transient, reduced-gravity, primitive-equation model for the simulation of transient bottom arrested gravity plumes was adapted from a wetting and drying model. The slice model consists of a turbulent lower layer with an upper layer at rest and it was compared with observations. Løyning & Weber (1997) also discussed the thermobaric effect where the thermal expansion coefﬁcient of sea water can change with pressure (i.e. depth). This effect can apparently have a signiﬁcant effect in shallow regions in polar oceans. However, Jungclaus et al. (1995) carried out the simulation with and without thermobaric effect and produced very similar results. They argued that this similarity may be due to the particular set of circumstances being modelled. Kämpf & Backhaus (1998) presented a 3-D non-hydrostatic model to examine convection in shallow sea coastal ice formation regions. The model included prognostic temperature and salinity, a nonlinear equation of state, a nonlinear equation for the dependence of the freezing point on salinity, different types of ice and the transition between the types as well as ice hydrodynamics. Brine release was also included and it showed the importance of short-lived atmospheric events. There was a comparison with a 2-D (slice) model which showed that the key results of the 3-D simulation could be captured. Nazarenko et al. (1998) used a MOM type model coupled with a snow ice model to examine tracer dispersion. The eddy parameterisation was important and the NEPTUNE effect was parameterised. This effect is due to eddies interacting with bottom topography yielding a driving force rather than damping. The theoretical basis for NEPTUNE has been discussed in a series of papers by Holloway (e.g. Holloway 1996). Its importance in the Arctic Ocean was demonstrated but in this simulation the presence of ice appeared to have little effect. Although it is in an oceanic context, the use of thermocline depth as a point of comparison was reported by Février et al. (2000) who carried out a comparison between the Hadley Centre OHAD model, the LODYC/OPA model and the HOPE (Hamburg Ocean Primitive Equation Model) which is formulated on the Arakawa “E” grid (although a new “C” grid form is being developed). It is a very large-scale detailed intercomparison which includes the use of different meteorological forcing. Lehmann & Hinrichsen (2000) used a special version of the GFDL model including a coupled ice model (the Hamburg Sea Ice Model) to study the transports of heat, salt and water in the Baltic. Again, the variability of the meteorological input was regarded as important. Evaporation and precipitation were taken into account. 90

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Hill et al. (1997) used a diagnostic model which aimed to maintain the balance between stirring and buoyancy. Two models were combined in effect, a heating-stirring model and a linear steady state circulation model. The model is a study of a gyre in the western Irish Sea. The same gyre is a subject of the model of Xing & Davies (2001b) in which a high resolution (3 km) σ-co-ordinate model with vertical eddy viscosity and diffusivity provided by a one-equation turbulence model was used. The model results revealed an ageostrophic component of the gyre. Brydon et al. (1999) presented a new, approximate, equation of state for sea water. The present UNESCO formulation is computationally expensive and so this paper gave a new polynomial form that can be directly evaluated. It is cubic in potential temperature, quadratic in pressure and linear in salinity and this should reduce computational cost. Two more idealised studies concerning the temperature cycle on the shelf have been reported. Prandle & Lane (1995) carried out a theoretical investigation to describe annual temperature cycle in shelf seas. Although the model is simple, based on a series expansion, it is solved numerically to allow for the overturning that occurs in autumn. One consequence of the model is that it infers that horizontal gradients at the surface will be less than at depth. Also, Prandle (1998) used a single point coupled ocean–atmosphere model incorporating an MY 2.5 scheme to study seasonal cycles with particular application to shelf seas. He showed that sea surface temperature and ambient air temperature are governed by localised equilibrium. In shallow water, the amplitude of the seasonal cycle is modulated by water depth and tidal current amplitude and large currents can reduce the amplitude. This is probably the ﬁrst quantitative estimate of the effect of shallow seas on adjacent coastal climates. Finally, as well as natural temperature effects, Gorny et al. (2000) commented on artiﬁcial heat sources that may be modelled. The thermal plumes from nuclear power plants may be useful for water quality monitoring, for example, and of course, they have a seasonal signiﬁcance.

Sediments The study of sediment erosion, transport and deposition is an entire scientiﬁc discipline. It may, however, be useful to survey those sedimentary models that have been used in conjunction with a coastal hydrodynamic model or have been incorporated in a modelling system. From the response to the questionnaire there were only a few models that actually incorporated sedimentary modules. More generally a sedimentary model was a separate module as part of a general modelling system. The TELEMAC system, the suite of programs from WL-Delft Hydraulics (e.g. DELFT2D-MOR) and the POLCOMS system can handle suspended particulate matter, the latter through advection–diffusion and/or the sediment varieties in the ERSEM ecosystem model. The model of Xing & Davies (2001b) does contain suspended sediment advection concentration and a pick-up function for sediment. Turbulence is computed using a range of turbulence closure schemes and the model has been applied to shelf edge sediment movement due to internal tides and winds. The CSERAM model of Aldridge (1998) is a dedicated sediment module to handle multiple sediments as well as erosion. The treatment of sediment can vary from a simple tracer representing suspended particulate matter (SPM) to complex formulations including erosion and deposition. Cohesive sediment may be subject to ﬂocculation and there may be other processes. Barros (1996) using a ﬁnite element model of the Tejo estuary to examine the evolution of sedimentary features included 91

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a further term for bottom consolidation. Also, sediment particles whether inorganic, organic, or mixed, can act as vectors for substances that may be adsorbed upon them. Even this particular process may need modelling (Hansen & Leckie 1998). Some reviews of sediment models may be found in the literature. Schoonees & Theron (1995), although strictly dealing with beach processes, reviewed 10 models that examined cross-shore sediment transport. They issued the caution that a complex model is only as good as its worst part and that the best overall model may not be the best for a speciﬁc application. Nicholson et al. (1997) discussed the performance of ﬁve different morphological models run on a test case of an offshore breakwater. These models couple wave, currents and sediment transport. A. G. Davies et al. (1997) compared four sediment transport models with laboratory data and cautioned that none of the models gave a good detailed description of the time-dependent suspended sediment concentrations because conventional turbulence diffusion schemes are unable to represent the entrainment of sediment into suspension by convective events at ﬂow reversal. Wu et al. (1998) demonstrated a 3-D layer integrated model of the Humber including cohesive sediment transport. In this model settling depends on sediment concentration as ﬂocculation can occur, so there are three types of settling: free, ﬂocculation or hindered. There are also two critical velocities for deposition and erosion. Between these two velocities an equilibrium, with no deposition or erosion is assumed. Similar considerations apply in many other sediment models. For example, Clarke & Elliott (1998) presented a 2-D depth integrated model of the Forth estuary in which a vertical log proﬁle for the tidal velocities was assumed. The SPM concentration follows a modiﬁed Rouse proﬁle. The best agreement was obtained if the erosion and deposition parameters were made functions of the spring-neap tidal cycle. Puls et al. (1997) used an SPM advection–diffusion model taking hydrodynamics, including vertical diffusivities, from a HAMSOM model of the North Sea in an attempt to extend the SPM data from the UK NERC North Sea Project. In this case it was discovered that the results were limited by the model resolution. This discovery was also emphasised in a follow-up paper of a model of the German Bight (Puls et al. 1999) which stated that better resolution of current and wave data was required. Also in the North Sea, Holt & James (1999b) presented SPM modelling using the model of Proctor & James (1996) with prognostic temperature. Comparisons were made with data from the NERC-North Sea Project using an inverse model. It was found that a tidal phase correction was important and that the model was sensitive to the frequency of meteorological forcing. However, 3-hourly inputs were deemed adequate. A sand budget model of the central zone of Holland was presented by van Rijn (1997) and combines a wave propagation model, a vertical ﬂow model and a sand transport model. Another North Sea paper is that of Gerritsen et al. (2000) who performed an SPM simulation in 2-D and 3-D. The 2-D POL2G advection–diffusion model is used to look at sources and sinks and then a full 3-D model experiment, with curvilinear representation, near-shore grid reﬁnement and the inclusion of wind waves was performed. A sensitivity analysis was performed and an inverse scheme used to identify sources. In the engineering ﬁeld the study of sediment transport, erosion and deposition may be very necessary. Mohan et al. (2000) considered design criteria for the capping of contaminated marine sediments. Jankowski et al. (1996) used the TELEMAC system to assess the residence time of a plume of sediment from potential mining off the coast of Peru. The transport of suspended sediment across and down slopes has already been seen in the model of Amin & Huthnance (1999). Fohrmann et al. (1998) used a coupled hydrodynamicsediment model to simulate low density turbidity plumes on the slope near Svalbard. The approach was to let the interface between the sediment plume and ambient water resemble 92

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a ﬂexible membrane. They emphasised that turbidity plumes can penetrate beneath strong thermo- or haloclines yet on deposition the plume may become buoyant again. This can lead to water mass formation and oxygenation of deep water. Similar features are seen in Kämpf et al. (1999) whose slice model couples hydrodynamics with sediment transport, settling, deposition and erosion. Sediment entrainment makes the plume denser and then detrainment makes it bouyant again. This process was further explored by Kämpf & Fohrmann (2000) and Kämpf (2000) who examined the effect on plume dynamics of submarine canyons and Coriolis. They used a modiﬁed σ-co-ordinate scheme which allowed good vertical resolution of the thin bottom layer in which the plume moves. A different type of plume was investigated by Arnoux-Chiavassa et al. (1999) who used a coupled model to examine sediment transport off the Rhône estuary. A sensitivity test to turbulent parameterisation is followed by a realistic simulation which is compared with observations The interest here lies in beach nourishment and shoreline evolution. Morphodynamic evolution modelling is one aspect of sedimentary study that requires an input from hydrodynamic models. Williams et al. (2000) compared observations of the Middelkerke sand bank in the southern North Sea with a 3-D model (3D-Bank) that combines hydrodynamics from Davies (1991), a refraction–diffraction model for waves from MacDonald & O’Connor (1996) and the 3-D sediment transport model of O’Connor et al. (1994). A net clockwise motion of the sediment around the bank was conﬁrmed by observations. De Vriend (1997) argued that a full 3-D description should be used in morphodynamic modelling and that model developments are therefore still required. Schuttelaars & de Swart (2000), however, use a 1-D model to examine the possible morphodynamic equilibrium of tidal embayments. Some idealised cases, approximating to the Western Scheldt, Netherlands, were discussed in the context of tidal motion. A series of tidal scenarios were tested, the embayment lengths varied and the possibility of tidal resonance examined. There are many kinds of technical features of sedimentary modelling that could be important in future. For example, the chemical behaviour of sediment may be important. Van der Loeff & Boudreau (1997) reviewed modelling of the vertical gradients seen at just above and below the sea bed that arise from the dynamic interchange between the sediment and the water column. The implications for chemical species including radionucleides was assessed. Jago & Mahamod (1999) presented a simple algorithm for high energy ﬂows suspending sediment. McManus & Prandle (1997) used principle component analysis on observations of the southern North Sea gathered during the UK NERC–North Sea Project to try to locate sources of suspended sediment. In this case the Wash was identiﬁed as a source of sediment. Then a numerical advection–diffusion model was used to simulate sediment dispersal, and erosion and settling were included. The model was tuned and then, in similar fashion to McManus & Prandle (1994), a multiple linear regression was used to determine the rates of sediment supply from these sources. Models may also be used to look at patterns of past sedimentation. Gerritsen & Berentsen (1998) used the depth-integrated DCSM model together with the Galapatti sediment transport model and the Engelund–Hansen formulation for sediment erosion to study tidally induced equilibrium sand balances in the North Sea during the Holocene period. Their approach incorporated three sub-models for tidal ﬂow, transport and bed height. Aldridge (1997) assessed the role of tidal asymmetry in transporting sediment using a model of Morecambe Bay in the eastern Irish Sea, based on the spectral ﬁnite-difference model of Davies (1987) with a 500 m grid resolution. By comparison with observations a close correlation was revealed thus demonstrating the tidal dominance in sediment transport. 93

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The model was also veriﬁed against radar measurements of surface current. As the eastern Irish Sea was an area of radionucleide discharge, Clifton et al. (1999) calculated a grain size proxy to aid the modelling and prediction of radionucleide activity in the coastal salt marshes and mud ﬂats of the area. Under very strong winds, such as occur in tropical storms, a bottom boundary layer containing large amounts of suspended sediment can form, which effectively reduces bed friction (Spaulding & Isaji 1987). However even the sediment load under normal tidal ﬂows can have an effect and this was examined by Alvarez et al. (1999) using a 2-D model of Cádiz Bay, Spain. Although generally at these lesser speeds the effect is almost negligible, nevertheless the time-space tidal characteristics become more variable, with an increase in the maximum depth-averaged velocity, and a decrease in drag coefﬁcient, during the ﬂood and ebb currents thus reducing tidal dissipation. Finally in this section, a brief mention must be made of models of sediment movement beneath waves or combined waves and currents. This is a topic for study in its own right but could become part of a larger scale modelling system of the coastal sea. For example, A. G. Davies (1995), using the one-equation turbulence closure scheme of Davies (1988), considered the effects of unsteadiness on sediment ﬂuxes under combined waves and currents. He noticed some phase lag effects. Also, A. G. Davies & Li (1997) made use of a oneequation turbulence scheme to investigate sediment transport beneath large, symmetric and asymmetric waves and combined wave and current ﬂow. One feature they noted was that sediment entrainment events accompany ﬂow reversal in the system.

Biology As a natural progression, many models that initially started as purely hydrodynamic ones now routinely incorporate other processes, including biological elements or entire ecological systems. Biological modelling is an entire scientiﬁc discipline in itself so in this section models that require some hydrodynamic input and which investigate the effect of the hydrodynamics on the biology will be examined. For example, Chen & Annan (2000) assessed the effect of different turbulence closure schemes on biological production in a 1-D model. Another case where physics may affect the biology may be seen in Lévy et al. (1998), who used the OPA model as the host physical model with an embedded primary production module. This idealised case assessed the effect of eddy resolution and they concluded that primary production can be underestimated by a factor of four without eddies. The representation of turbulence may be parameterised. For example in the model developed by Marguerit et al. (1998) to simulate the diffusion of copepods in a 1-D case, they represented ocean turbulence as cascading fractals. Pinazo et al. (1996) used a coupled physical and biogeochemical model to examine the variability of phytoplankton in upwelling areas of the northwestern Mediterranean. This study is based hydrodynamically, and appropriately, on the Johns et al. (1992) model which was developed to study upwelling in this region. The sensitivity to sinking rate was assessed. Although biological models are being included directly in models of the coastal and shelf seas or as an external module, from the questionnaire only about 20% of respondents actually reported some form of biological model in association with their hydrodynamic model. Within the 20% there was little consensus on the type of model used. In certain cases, the biological component consisted of the advection–diffusion of nutrients, in other 94

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POLCOMS – schematic Met Ofﬁce operational forecasting

Meteorological forcing

Tidal forcing

Met Ofﬁce Ocean Model forcing

Climatology and extreme statistics

3-D baroclinic hydrodynamic coastal ocean model

Fish larvae modelling

Figure 5

Contaminant modelling

ERSEM biology

VISUALISATION & DATA BANKING

Sediment transport and resuspension

Schematic of the POLCOMS system.

cases more complex biological systems with many state variables and associated processes such as sediment resuspension were included. Surveying the literature for biological models in connection with hydrodynamics, an early contribution was Turing (1952) reported by Malchow (1996), who showed that the interaction of nonlinear reactions and diffusion can give rise to spatial structure and hence the patterns seen in the spatial distribution of plankton. A prominent model is the European Regional Seas Ecosystem model ERSEM which was originally applied to the North Sea (Baretta et al. 1995, Baretta-Bekker et al. 1995). Zavatorelli et al. (2000) coupled ERSEM with the POM model to examine in idealised form the ecosystem dynamics of the Adriatic. Usefully this paper gives a brief overview of ERSEM and some recent modiﬁcations as well as some of the principles involved in coupling the two models together. The dependence of the ecosystem on the hydrodynamics and the riverborne nutrients is demonstrated. An earlier model using ERSEM to study the Adriatic was a 1-D vertically resolved case (Allen et al. 1998) using the vertical diffusion sub-model of POM. The model was able to simulate the climatological seasonal cycle and showed phosphate to be the limiting nutrient. More recently, ERSEM has been incorporated in the POLCOMS system (Fig. 5) in realistic simulations using realistic boundary inputs from an ocean model as well as realistic meteorological inputs and river ﬂows. A sample output showing the time of the chlorophyll bloom and its surface maximum value is shown for the year 1995 in Figure 6. Another ecological model is the NORWegian ECOlogical Model system, NORWECOM. This physical basis is a version of the POM model and the biological model is coupled to the physical model through subsurface light, the hydrography and the horizontal and vertical movement of the water masses (Skogen 1993, Skogen & Soiland 1998). NORWECOM, HANSOM as well as observations, were used by Laane et al. (1996) to examine the ﬂuxes of the nutrients, N, P, Si into the North Sea from the Atlantic Ocean, the main aim being to get as precise a measure as possible of ﬂuxes. Skogen & Moll (2000) compared NORWECOM and the Hamburg ecological North Sea model, ECOHAM1 (Moll 1997, 1998, 1999) and examined primary production and interannual variability. Several sensitivity tests were run. The study revealed the reliance on the underlying hydrodynamics 95

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Time of Chlorophyll bloom (in days from start of year)

Surface Chlorophyll Maximum (mg. Chl m–3)

Figure 6 Chlorophyll distributions for 1995 from the coupled POLCOMS system. By courtesy of J. T. Holt-Proudman Oceanographic Laboratory.

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which, in ECOHAM1, is based on a version of HANSOM (Pohlmann 1996a). A full review of 3-D ecological modelling related to the North Sea shelf system is in preparation (Moll & Radach 2001). A recent example of a fully coupled hydrodynamical–ecological model is the COHERENS model (Luyten 1999), which was documented and disseminated under the EU-COHERENS project. It was based on a framework idea of Wolf (1991) and was realised as the MU-ROFI model (Ruddick 1995, Ruddick et al. 1995), and was used in the EU-PROFILE project (Huthnance 1997a,b). The biological component originated in a 1-D three-layer model for microbiological processes in shelf seas (Tett 1990). This component was later embedded in a three-layer physical framework and named L3VNP (Tett & Walne 1995). The paper by Tett & Wilson (2000) gives an overview of the development of these models. It is interesting to note that the COHERENS model as disseminated is accompanied by several test cases, some purely physical, some mixed and others purely biological. The accompanying user guide documentation extends over 914 pages and warnings are given where expert knowledge is required to change default parameter settings. Occasionally more than one biological model may be incorporated. For example, the AQUAPHY phytoplankton model of Lancelot et al. (1991) and the HSB organic matter degradation model of Billen & Servais (1989) have been coupled with the LODYC/OPA model of Tusseau-Vuillemin et al. (1998). In this case the transport and biogeochemical inﬂuences on nitrate ﬂuxes in the Gulf of Lions are examined. There are several other models with physical and biological components, Semovski et al. (1996) demonstrated both 1-D and 3-D models of the Gulf of Gdansk, Poland to examine phytoplankton population dynamics accompanying an abnormal spring bloom. Also in the Baltic, Fennel (1995) presented a model of plankton dynamics in which the physics was reduced to the formation and destruction of the thermocline in spring and autumn. Fennel & Neumann (1996) coupled the MOM version 1 model with the Fennel (1995) model to show patchiness controlled by mesoscale circulation in conjunction with the sinking of plankton and nutrient limitation. In a further study using a similar scheme, Fennel (1999) examined phytoplankton spring blooms in the western Baltic, which revealed the inﬂuence of convective mixing on the timing of the spring bloom. Yet another model used by several authors is the ELISE interactive biological model presented by Ménesguen (1991), which allows results from a hydrodynamic model to be coupled with biological equations. It is used by Le Pape & Ménesguen (1997) where the results from a 2-D hydrodynamic model is used to build a system of 15 boxes representing the Bay of Brest. The application of ELISE showed that the ecological pressure was bearable at present but further environmental pressure could affect phytoplankton stocks. The ELISE system was also used by Guillaud & Ménesguen (1998) and Guillaud et al. (2000) to examine the phytoplankton in the Bay of Seine area. A similar system, ECOS was developed by Harris et al. (1984) primarily for the estuarine environment. This has now become an interactive modelling system ECOS3 available to commercial and academic users (Proctor et al. 1999, Pham et al. 1997, Liu et al. 1998). Kögler & Rey (1999) state that the Barents Sea is an area of high biological productivity and an important nursery ground. It may therefore be an important subject for the modelling of primary productivity. Furevik & Folvik (1996) also comment on the productivity of the Barents Sea and proceed with a model investigation using the method of Davies (1990) and Nøst (1994). Vertical tidal current proﬁles were derived in the Barents Sea near the critical latitude where the inertial frequency is close to the M2 frequency. This study showed an 97

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increasing benthic boundary layer thickness near the critical latitude but there was not a very clear cut inﬂuence on nutrients. Sometimes a paper may indicate a modelling component that could be useful in future. For example, the model of Pozdnyakov et al. (1998) relates water colour to biological parameters and looks at the consequence for the remote sensing of sea surface colour. This could become part of a future biological model. The GHER model has been used in different ways in conjunction with biological modules. Lacroix & Nival (1998) coupled a 1-D version of GHER including wind stress and heat ﬂux with the Laboratoire d’Ecologie du Plancton Marin (LEPM) food web model to estimate the effects of meteorological variability on primary production in the Ligurian Sea. Different frequencies of wind and solar input were tested and other sensitivity tests were carried out. One ﬁnding was that averaging wind stresses gives a poor representation of the seasonal phytoplankton cycle. The deeper the mixed layer the greater the production. The GHER model was used in 3-D in the Black Sea to drive an ecosystem model using a nitrogen cycle with 13 state variables (Grégoire et al. 1998). This is an initial assessment of coupling methods and the sensitivity of the biology to the physics was tested. Delhez (1998) also used a 3-D GHER model with a biological sub-model. A novel feature was that it operated in a macroscale spectral window, timescales of a month or so, to allow a reasonably complex biological model, in this case that of Varela et al. (1992), to be incorporated in detailed hydrodynamics yet without heavy computational cost. Another economical modelling method, in this case, of phytoplankton in the English Channel, has been mentioned earlier (Hoch & Garreau 1998). Ecosystem modelling of the Black Sea was also described by Oguz et al. (1996), who used a 1-D version of the POM model driving a biological model through the turbulence derived from an MY level 2.5 scheme. This model is used to simulate the main seasonal cycle in the Black Sea. Napolitano et al. (2000) used a 1-D coupled physical–biological model similar to Oguz et al. (1996) to demonstrate the biological importance of the Rhodes cyclonic gyre in the eastern Mediterranean. The importance of cyclonic areas is also seen in Crise et al. (1998) and Crispi et al. (1999) in successive papers where they used a 3-D MOM based model to carry out a lower trophic simulation of the Mediterranean and in particular the nitrogen cycle. They used a biharmonic horizontal eddy diffusivity and the ecosystem model is an aggregate model based on inorganic nitrogen, phytoplankton and detritus. In another study of biological signiﬁcance Oguz et al. (2001) examined redox cycling across the suboxic/anoxic interface zone in the Black Sea using hydrodynamics based on POM from Oguz & Malanotte-Rizzioli (1996). Recently, Smith & Tett (2000) introduced the SEDBIOL model, a 1-D depth resolving model that couples physical microbiological, sediment and re-suspension models, which they used to simulate conditions at the Goban Spur at the edge of the European continental shelf. The physical model is from Sharples & Tett (1994). Chen & Annan (2000) combined the physical model of Sharples & Tett (1994) with the thermodynamics of Warrach (1998) and the biological model of Tett & Walne (1995) to demonstrate the effect on biology of different turbulence closure schemes. Box models have also been used, in a series of papers by Rippeth & Jones (1997), Watts et al. (1998) and Simpson & Rippeth (1998) to examine non-conservative nutrients in Scottish sea lochs and the Irish Sea and the inﬂuences on the distribution of dissolved inorganic nutrients. Bergamasco & Zago (1999) used a multi-box ecosystem model connected to the nested M2 tidal hydrodynamic model of Boscolo & Bergamasco (1996) to 98

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examine the nitrogen cycle and microalgae in the lagoon of Venice. In some cases hydrodynamic models may be dispensed with altogether. For example, Oguz & Solihoglu (2000) used observed hydrodynamics for their 3-D biological model of the Black Sea. Tamsalu & Ennet (1995) and Ennet et al. (2000) reported developments of the FinEST ecohydrodynamic model. This model has been used in the Gulf of Riga to examine the inﬂuence of up- and downwelling and entrainment on the ecosystem components. Apparently, the vertical transport is more intensive if the inﬂuence of the Baltic is added to the motion within the Gulf with a very signiﬁcant effect on nutrients and phytoplankton growth. Finally, Bach et al. (1997) used an ecological model based on MIKE 21 hydrodynamics to examine the environmental impact of the construction of the Denmark–Sweden bridgetunnel. The results of increased turbidity, due to spillage from construction work, on surrounding beds of eelgrass was assessed. The effect of biology on ﬂow was also examined by Verduin & Backhaus (2000), who showed the inﬂuence of plants on the ﬂow over them. The interaction with the ﬂow could have effects on dispersion.

Wind waves Surface wind waves, if present, may be a very important component in any hydrodynamic model because their inﬂuence is felt in many ways. Their presence can affect sea surface roughness and hence have an effect on the wind stress. They also inﬂuence the turbulence induced at the surface, which can affect mixing and the thermocline depth. In shallow water, where there is a non-zero orbital velocity at the sea bed, they can enhance bed stress. They can also affect the turbulence in the water column and its effective eddy viscosity as well as adding to the processes that may suspend sediments. It is beyond the scope of this review to examine all the methods of modelling waves. Here the concern is with the diversity of wave models that are used in conjunction with hydrodynamic models.

Wave models A very prominent model in use in Europe is the WAM, third generation, spectral wave community model primarily for deeper and intermediate water depths. The most recent version is the WAM cycle 4 (Komen et al. 1994). This model has been constantly upgraded and improvements incorporated (Monbaliu et al. 2000). Examples of the implementation of WAM in the North Sea at various grid resolutions are given by Monbaliu et al. (1999) where wave effects on erosion and sedimentation are discussed. Ozer et al. (2000) combined the WAM model and a hydrodynamic model into a general purpose framework which is a generic module for dissemination. Guan et al. (1999) suggested an improvement to WAM by considering the radiation stresses due to wave/current interaction off the Rhône estuary. In that study they used the model of Arnoux-Chiavassa et al. (1995) as hydrodynamic background. The paper of de las Heras et al. (1995) examined the question of data assimilation into WAM using idealised test cases with the conclusion that a successful scheme is possible. A comparison between the WAM and the operational wave model run at the Met Ofﬁce (UK) is presented by Bidlot & Holt (1999) who showed that each model has merits under different conditions. Davies et al. (2000) used the WAM model to supply signiﬁcant wave height to two models at 12-km and 1-km resolution to investigate an oil spill event to the south of the Shetland Isles. In this method wave-dependent viscosity was included in the 99

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surface layer and it was shown to have an important contribution to the magnitude of the surface current and hence to the simulation. Johnson et al. (1999) discussed the usefulness of a wave model when coupled with a surge model. In a demonstration of the ECAWOM fully coupled atmosphere–wave–ocean model, from analysis, problems were revealed with WAM in shallow, fetch-limited areas. For such shallow water situations, the SWAN model has been developed. It was announced by Booij et al. (1999) and a veriﬁcation presented by Ris et al. (1999). Gorman & Nielsen (1999) carried out a comparison of SWAN results with observations and make some suggestions concerning the four-wave interaction term. Apart from these two models there are several more in use in Europe. It is of interest that in the responses to the questionnaire very few models were reported as having an intrinsic wave module. Therefore, there must be almost exclusive reliance on external wave models. For operational use, Flather (2000) reported the use of WAM and a reduced area version for the Netherlands NEDWAM. Until recently the Deutsche Wetterdienst has been using the second generation HYPA and HYPAS wave models and Météo France use the VAG wave model. Fradon et al. (2000) compared a modiﬁed form of the second-generation VAG model with the third-generation WAM model and produced very similar results. The modiﬁcation to the VAG model was the inclusion of the growth and dissipative terms of WAM instead of parameterisations. Johnson & Kofoed-Hansen (2000) used the third-generation model MIKE21 OSW3G to examine the inﬂuence of bottom friction dissipation on waves in shallow water which can inﬂuence sea surface roughness. A parametric discrete spectral model MIKE 21 NSW is presented by Johnson (1998) and the model is a compromise between too simple a description of waves and a full WAM type of model. Comparison with observations showed it performed reasonably well in shallow and fetch-limited situations. The use of a wave model may not be signiﬁcant in all instances, because Alvarez Fanjul et al. (1998) used the PCM-HAMSOM model with WAM and found it was not a signiﬁcant advance over simple wind stress schemes. Another model reported by Schneggenburger et al. (1997, 2000) is a spectral shallow water wave model, the “k” model. They compared it with the WAM model and ﬁeld data, and applied it to the Sylt-Rømø Bight. In this case the hydrodynamic model TRIM2D was used as background and signiﬁcant wave-tide effects were noted. There was some discussion as to the mechanism causing this interaction. Wave-tide effects were also considered in Kagan & Utkin (2000) using a 1.5 level model of the oscillatory turbulent bottom boundary layer. However, they considered that the interaction effect was quite small in shallow water environments and so the oscillations of the bottom friction stress, due to the two effects, are uncorrelated. Jones’ (2000) quasi-steady analytic model failed to reproduce observed tidally induced variation in wave parameters but the use of a linear ray-tracing model improved the simulation and it was concluded that short period waves respond to variation in current refraction over a tidal cycle whereas long period waves are more inﬂuenced by water depth variation. Two other wave models that must be mentioned are the WAVEWATCH and TOMAWAC models. Like WAM, the WAVEWATCH model is aimed primarily at oceanic applications but with different formulations. This model has undergone recent improvements and is now designated WAVEWATCH II (Tolman & Chalikov 1996). The TOMAWAC is another third generation model distinguished by the fact that it has been applied on an unstructured grid (Benoit et al. 1996). This model should be suitable for both oceanic and shallow water applications. Albiach et al. (2000) nest WAVEWATCH II and PROPS, a 100

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monochromatic wave propagation model, within the WAM model for local scale wave simulation in connection with operational forecasting on the Iberian coast.

Effect on sea roughness The effect of the presence of waves on sea surface roughness could be of importance especially when considering wind drag coefﬁcients. Roughness itself may need investigation. Johnson et al. (1998) analysed results from a set of observations in Danish waters in terms of the Charnock (1955) parameter, which is a dimensionless parameter that can evolve in time and which is characteristic of the sea roughness. This was compared with analyses using other roughness models. In a follow-up paper Johnson et al. (1999) used the WAM model to analyse the same data. Makin & Kudryavtsev (1999) reported a wind-over-waves coupling scheme to be used in a coupled wind–waves atmosphere model. It follows the approach where the momentum and heat ﬂuxes may be related to the sea state. The momentum exchange coefﬁcient (the sea drag) is related to the waves. Short gravity and capillary waves play a signiﬁcant role in extracting momentum from the atmosphere. In a subsequent paper by Kudryavtsev et al. (1999) attention was paid to the spectrum of the short wind waves. The model reproduces the wind speed dependence of the drag coefﬁcient. Although it may be desirable to improve the prescription of sea surface roughness due to waves, especially in surge models, this may be an unnecessary reﬁnement in certain circumstances. For example, Alvarez Fanjul et al. (1998) coupled the HAMSOM model with the WAM model in simulating surges on the Iberian Atlantic coast but found little signiﬁcant improvement over standard wind stress formulations.

Wave/current interaction Turning to wave/current interaction, Davies & Lawrence (1995) and Davies & Jones (1996) showed the inﬂuence of waves on bed stresses and ﬂow in an idealised case using the method of Signell et al. (1990). This method was then adopted for a realistic surge event in the eastern Irish Sea and it demonstrated that wave/current interaction could have a signiﬁcant effect on surge levels in certain areas, in this case at Liverpool. Wave/current interaction is also considered by Glorioso & Davies (1995) where in an idealised model of the Bristol Channel, they considered the impact on sediment suspension and the ﬂushing time of the Channel. Deigaard et al. (1998) examined and formulated calculations of the momentum exchanges due to combined waves and current and showed its signiﬁcance in two scenarios. Wolf & Prandle (1999) used data from the SCAWVEX project to investigate the effect of waves on currents and currents on waves. Their conclusion was that the effect of waves on currents causes a decrease in tidal current amplitude, whereas the effect of currents on waves results in a tidal modulation of wave height and period. This effect is quantiﬁable. Another wave–current study is presented in Büchmann et al. (1998) where a numerical wave tank is considered in which the interaction between waves, currents and a speciﬁed structure is simulated by a 3-D boundary element model in the time domain. Kantardgi (1995) dealt with the propagation of waves through an area containing currents with a non-uniform velocity proﬁle. An approximate formulation for the practical determination of the wave characteristics was derived from analytic forms. Finally, there are other ways in which the representation of waves and inﬂuences upon them may be relevant in numerical models. We can note some particular effects. 101

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There may be biological inﬂuences on waves. Mendéz et al. (1999) considered the effect of vegetation on surface waves and Möller et al. (1999) modelled wave attenuation due to salt marshes. Such models could provide evidence for the use of salt marshes as a sea defence. MacDonald & O’Connor (1996) used a numerical model to study the effect of sealevel rise on the wave climate at a coast next to a linear sand bank representing the Flanders coast. Using an assumed sea-level change scenario, a 10% rise in wave activity was predicted over 130 yr. Dotsenko (1998) performed a numerical analysis of a further type of wave that may be occasionally found in the seas of Europe. In this case it is a study of tsunami waves in the Black Sea as they propagate on to the South Crimea shelf. Péchon et al. (1997) reviewed seven numerical models that simulate waves and currents in the surf zone and they are tested against experimental results. It was discovered that all the formulations tested systematically overestimated the radiation stress and highlighted the need for an advanced turbulence model in the surf zone. The paper by Oliveira & Anastasiou (1998) described an efﬁcient numerical model of the mild-slope equation that describes water wave propagation in coastal regions (combined refraction–diffraction) based on robust iterative methods. From tests they found that the Bi-Conjugate Stabilised (Bi-CGSTAB) method converges to solution better than the Generalised Minimum Residual (GMRES) scheme. Rozhkov et al. (2000) used a model is to calculate wind wave heights over many years. An annual rhythmicity was detected and a probability model was evolved for time series of wave heights in the range of their synoptic scale variability with annual rhythmicity taken into account. This scheme was applied to the Baltic, Barents, Black and Mediterranean Seas.

Internal waves The presence of internal waves and tides may be signiﬁcant because they can contribute to the mixing within a system. Sometimes internal waves may be simply parameterised by an addition to the model diffusivity or, if the model resolution is ﬁne enough, they may actually be resolved. From the survey, most models did not explicitly carry a description of internal waves. The MOHID-3D model (Villareal, pers. comm.) uses a simple algebraic model for providing eddy diffusivities below the mixed layer. The POL3DB model uses an extra term in the turbulent kinetic equation following Mellor (1989) but a ﬁne resolution version of the model was able to resolve the internal tides (Proctor & James 1996). Another model (Vilibic & Orlic 1999) consists of a two-layer, 2-D rectangular basin which is used to interpret surface seiches and internal Kelvin waves observed off Zadar in the eastern Adriatic. Internal waves and tides have been the subject of Bousinnesq or Korteweg-de Vries models. A Boussinesq model examining the generation of solitons and internal tides in the Celtic Sea, Massachussetts Bay and the Iberian shelf is demonstrated by Gerkema (1996) and Jeans (1998). A Korteweg-de Vries model is used by Talipova et al. (1998) to examine internal tides and waves in the Baltic and a similar model is used to look at internal waves in the Strait of Gibraltar (Brandt et al. 1996). Here, the Korteweg-de Vries numerical model is compared with satellite synthetic aperture radar images. Some non-European scenarios (Australian coast, Hawaiian ridge) have also been modelled (Holloway et al. 1997, 1998, 1999). Although internal waves and tides may contribute to mixing, an analysis by Thorpe (2000) showed that mixing near topographical features such as a slope may not be due just 102

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to internal waves but also to the effect of rotation. The Malin shelf continental slope west of Scotland has been the subject of a series of modelling studies (Xing & Davies 1998b,c, Davies & Xing 2001). In the last case a ﬁne resolution 3-D model of the shelf and slope is forced by an M2 tide. The σ-co-ordinate model with turbulence closure scheme is used to test the sensitivity of the system to shelf width and the location of shelf edge ﬂow. Xing et al. (1999) found that the annual temperature cycle at the shelf edge was improved when mixing due to internal tides was included in their model. It was inferred that this mixing could have biological consequences. In a semi-analytic study Donato et al. (1999) considered the focusing of surface gravity waves due to the surface currents induced by internal waves. An interesting mechanism for the generation of internal waves was reported by Bertram et al. (1999) due to the motion of an iceberg in a pycnocline.

Data assimilation The survey also enquired whether data assimilation was available and the responses showed that there were very few modellers who reported the use of even indirect techniques. For example, in two cases sea-surface temperature was indirectly assimilated by means of an atmospheric model which did have data assimilation. In one application a simple nudging technique was used with the GHER model, and the POLCOMS system allowed the use of satellite derived sea-surface temperature. There were two exceptions. First, the HYCOM model as used at the Nansen centre reported a full implementation of an ensemble Kalman ﬁlter with optimal interpolation (Evensen 1997, Evensen & van Leeuwen 2000). Second, the FVM model had an assimilation scheme on a test basis for elevation measurements. In this method, steady Kalman gain ﬁelds were applied based on either an ensemble Kalman ﬁlter or a reduced rank square root Kalman ﬁlter. Even though few models were reported as using assimilation schemes, the literature on data assimilation methods, especially in connection with operational methods, is very large. It is assumed therefore, that only in operational systems or near operational systems are such techniques required. For example, Breivik & Sætre (2001) report the use of nested models including assimilated data from H.F. radar in an operational scheme for shipping. Most model developers therefore may be interested only in data for model veriﬁcation. However, a few “non-operational” papers that do discuss various forms of assimilation are summarised below. Data assimilation may generally incorporate inverse methods such as that of Copeland & Bayne (1998) where data is used to help build a model. They use a cost function scheme in conjunction with an idealised study and give a full formulation of the inverse model. The actual data that may be used for developing and verifying models may need a set of guidelines as to scope and format. A set of such guidelines was presented by Lane et al. (2000). The complementary nature of models and data were referred to by Sarkisyan (2000) as the “mutual aid” between models and measurements. He presented four methods of synthesising these two components: the dynamic, the diagnostic, the adaptive and the 4-dimensional. He emphasised the promise of the last approach. Data may be used to test and verify models but models themselves may be used to extend or help to interpret datasets. This was the aim of Puls et al. (1997) to extend the SPM data from the U.K. NERC North Sea Project. Engedahl et al. (1998) discussed and demonstrated the use of a numerical model to enhance climatological archives of the Nordic Seas. In this case the ECOM3D model was used. In another 103

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study, the HAMSOM model was used to model the mesoscale features in the German Bight to complement and help to interpret the observations from the KUSTOS experiment (Becker et al. 1999). Naturally, satellites are a prime source of data that may be used for assimilation in models. Johannessen et al. (2000) reviewed the use of such data in operational oceanography emphasising the sparseness of certain types of data due to cloudiness. Woodgate & Killworth (1996) using theoretical and numerical models demonstrated that satellite altimetry is unsuitable for deriving the vertical ﬂow structure in a functional type model because of the extreme sensitivity to the barotropic mode. Drakopoulos et al. (1997) using a MOM 1.0 rigid-lid model carried out a series of twin experiments assimilating surface pressure to represent altimeter measurements. Wind data at different frequencies of input were also supplied. Several conclusions followed. For example, if the barotropic mode was allowed to converge before assimilating data, a bottom pressure update was unnecessary if the correct wind stresses were known. Annan & Hargreaves (1999) demonstrated the assimilation of satellite sea-surface temperature in the model of Proctor & James (1996) using a simpliﬁed Kalman ﬁlter approach which improved the predictive ability of the model in a series of numerical, annual cycle, experiments. An integrated approach using SPM concentrations derived from satellite data was presented by Vos et al. (2000). A goodness of ﬁt criterion was established and a curvilinear model of the Dutch coastal zone was used as a testbed. The scheme proved successful and it may help in reducing errors in SPM budgets. Integration can proceed at a deeper level. Kurapov & Kivman (1999) pointed out that the ﬁnite element method for solving differential equations and the data assimilation technique of generalised inversion have a natural afﬁnity as they both require a minimisation technique. This relation was demonstrated in a tidal model of the Barents Sea. Incidentally, this method of data assimilation means that conventional boundary conditions from an exterior model are not needed. The representer function approach is sometimes used in data assimilation. It was used by Le Provost et al. (1998) to assimilate altimeter data from the TOPEX/POSEIDON satellite to improve the tidal representation of a global ﬁnite element model. Although this is an oceanic model, such assimilation could be important for the improvement of boundary conditions for coastal models. The representer, or optimal inﬂuence function, approach was also used by Echevin et al. (2000) who commented that in the coastal zone, due to nonlinear processes and the complexities of the bathymetry and the coastline, data assimilation may be a more complex process than in the ocean. They used a POM model of the French coast in the northwest Mediterranean and applied a statistical method in conjunction with an Ensemble Kalman ﬁlter technique to improve assimilation in such regions. They emphasised its importance for altimeter data. In an earlier paper, Gekeler (1995) applied a data assimilation approach due to Zahel (1991), hitherto used only in ocean depths, to the shelf and improved the results from the WCM model of Davies & Jones (1992). The adaptive Kalman ﬁltering approach was considered and extended in a paper by Hoang et al. (1997) who proposed a new approach to assimilation. They introduced a low dimension parameterisation of the gain matrix which reduces computational cost. This method was tested in an idealised case and in a four-layer version of the MICOM model. A very extensive data assimilation experiment was described in Brankart & Brasseur (1998) who used a data pool of 100 000 station proﬁles collected in the Mediterranean to prepare a climatological analysis using a variational method and a ﬁnite element technique. The GHER model in barotropic mode was then used to adjust diagnostically the sea-surface 104

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elevation according to the thermohaline signal. A full 3-D prognostic simulation was then performed and compared. The actual techniques of data assimilation were considered by Lellouche et al. (1998) who discussed the problems in the discretisation of the direct and adjoint models used in assimilation methods. They demonstrated a methodology of proceeding from the continuous to the discrete form and tested this using an idealised 3-D model of a multilayered ocean. Finally, a brief word about assimilation in wave models. De las Heras et al. (1995) considered the impact of data assimilation in the WAM model and demonstrated the feasibility of an adjoint method in several idealised cases. They looked forward to the synthetic aperture radar (SAR) data from the ERS-1 satellite. In a later paper Breivik et al. (1998) reported on a 4-month test using such data and its impact on the operational wave model used by the Norwegian Meteorological Institute. They found that strict data control was necessary to avoid excessive noise and that the present implementation provided only a marginal improvement. They, in turn, looked forward to better data becoming available from future satellites.

Operational oceanography The ultimate test of our understanding of a system and our ability to model it lies in being able to predict accurately its future behaviour. Originally, within Europe the development of predictive or operational models arose from a real need to give warning of disastrous storm surge levels. In the UK an early version of a numerical model scheme commenced in 1978 taking its input data from the Met Ofﬁce (UK) atmospheric model. A recent survey (Flather 2000) gave a comprehensive overview of the development of numerical model storm surge forecast schemes in several countries for the northern and western coasts of Europe. Indeed it is interesting to see that apart from national agencies there is now international cooperation amongst nations on operational schemes such as HIROMB (Funkquist & Kleine in press). Incidentally, it may be of interest to contrast these European systems with those operational around the coasts of the United States (Haidvogel et al. 2000). Initially basic water level predictions (tide + meteorologically-induced sea-level change) were sought but increasingly wave modelling was added and Flather (2000) also gives an overview of the wave models used in various European operational schemes. Details of some of these models have been seen in previous sections of this review. Flather (2000) sees the ﬂow of information between the various operational systems as a key requirement for continuing development in this ﬁeld. The importance of operational oceanography within Europe has been highlighted by such joint projects as PROMISE (Pre-Operational Modelling in the Seas of Europe) (Prandle 2000a) which provided a forum looking primarily at the hydrodynamic and sedimentary aspects of operational forecasting. In a wider forum, operational oceanography is very much a concern of the EuroGOOS collaboration. Improved communication has again been highlighted ( Woods 1997). A further project underway is ESODAE (European Shelf Seas Ocean Data Assimilation and Forecast Experiment ). It is a concerted action to examine existing schemes and then to carry out a real-time experiment including data assimilation. At present it has the status of a European Commission Concerted Action to develop a plan to carry out a real-time experiment including data assimilation in the context of the Global Ocean Data Assimilation 105

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Surge elevations at Sheerness 1.0

Height (m.)

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0.0 MO(UK) MUMM DMI KNMI BSH DNMI

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0

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20

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Time from 0h 11/04/2001 (in hr)

Figure 7 Time series of forecast surge elevations at Sheerness (UK east coast) from the operational models of the Met Ofﬁce (UK), MUMM (Belgium), DMI (Denmark), KNMI (Netherlands (data not available on this particular day) ), BSH (Germany) and DNMI (Norway). Figure courtesy of M. Holt, Met. Ofﬁce (UK).

Experiment (GODAE). One particular by-product of the ESODAE forum is an exchange of predictions between the North Sea agencies running storm surge models. As a result of the ESODAE initiative, the Met Ofﬁce (UK), MUMM (Belgium), DMI (Denmark), BSH (Germany), DNMI (Norway) and KNMI (Netherlands) are now routinely exchanging information on operational sea-level predictions. Figure 7 shows a typical time series. This exchange of information is a valuable resource because it provides back-up and can act as an ensemble forecast. It is unclear whether a single “best” model will emerge in future. Experience from other European projects such as NOMADS (Proctor 1997) and NOMADS2 suggest that different models may give the closest representation of nature at different times and occasionally the outlier of an ensemble of predictions may, in fact, be the closest to reality. In such operational schemes, there is a constant drive towards improvement and enhancement. For example, in the UK the original 35-km resolution model of 1978 was superseded by a 12-km model in 1991 (Flather 2000). However as seen in earlier sections there are increasing needs for the prediction of other parameters such as temperature and salinity, which imply density effects and therefore phenomena such as stratiﬁcation. In the UK, components of the POLCOMS system (see next section) have been run operationally at the Met Ofﬁce since 2000 providing full 3-D temperature and salinity forecasts for the northwestern European shelf. As well as upgrades or enhancements, individual components of existing operational schemes may be examined from time to time. For example, Bidlot & Holt (1999) compared the Met Ofﬁce (UK) wave model and the WAM model run at the European Centre for Medium Range Weather Forecasting. Strategies for future inclusion in operational schemes may also be proposed. Johannessen et al. (2000) examined the use of satellite earth observations for data assimilation in operational oceanography, where they 106

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considered problems such as how to deal with cloud cover which can, at times, prevent sensing of the ocean surface. In a more localised sense, an operational scheme under the EUROROSE project is reported by Breivik & Sætre (2001). It provides real time and forecast current information for ship trafﬁc on the Norwegian Coast using H.F. radar. The data is assimilated into a set of nested POM-based models at 20-km, 4-km and 1-km resolution. Elsewhere in Europe, MFSPP (The Mediterranean Forecasting System Pilot Project) is attempting to forecast the ecosystem of the Mediterranean using a version of POM coupled with the ERSEM model and with local models, the one-dimensional 1DV (BLANES) model in the Catalan Sea and the 3-dimensional FINEST model on the Egyptian shelf. In Greece, the National Centre for Marine Research hosts the POSEIDON system which provides forecasts for the Aegean, coupling observational data from many sources including a network of buoys, with a numerical model, based on a version of POM, of the Aegean nested in an Eastern Mediterranean model (Soukissian et al. 1999, Nittis et al. in press). A nowcast/forecast system for the whole Mediterranean was described by Horton et al. (1997) and consists of a POM based system including thermohaline effects. This scheme uses data assimilation, primarily from satellite sea-surface temperatures and expendable bathythermograph measurements. Nowcast systems with constant monitoring of a model can help in highlighting any deﬁciencies that may cause a model to deviate from reality. As well as these more traditional schemes of operational forcasting there have been attempts at dispensing with the models altogether. For the Alboran Sea, Alvarez et al. (2000) suggested using empirical orthogonal functions to predict future sea-surface temperature patterns directly from meteorological inputs. Another approach to improve forecasts may be the use of neural networks either directly or as an interpreter of numerical model output (Röske 1997, Greve & Lange 1999, Babaric et al. 2001). The future of operational modelling in Europe has been considered by several authors such as Prandle (2000b) and Flather (2000). In such modelling there is a natural progression towards fully coupled systems containing physical, chemical and biological processes. This coupling has been envisaged for some time (e.g. Aksnes et al. 1995) and will be discussed in the section that follows.

Discussion This review set out with the intention of illustrating the diversity of models in the European context and we can now re-examine the questions posed in the Introduction.

Is such a multiplicity of models necessary? This question echoes the discussion in Flather (2000) with reference to operational modelling schemes. There are two points of view, either a single complex multinational model may develop, run by a single agency, which would prevent overlapping of work and beneﬁt from superior centralised facilities and state of the art developments. Alternatively, having a number of models provides back-up, is a cross-check on individual models and diversity could imply robustness. In the review of future data and modelling needs, Berlamont et al. 107

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(1996) suggested that resources might best be concentrated on one, or at most a few, models. A further contribution to this discussion also comes from Nihoul (1994), Davies (1994) and Salomon (1994). In this discussion the question arises whether one should attempt to develop as complicated a model as possible to ensure that as much of the processes in nature are included, which infers that the model should be as realistic as possible. Davies (1994) argued that simpler models still have a role, that they can provide insights as well as, of course, the usual considerations that they can save computational effort. This discussion contains many interesting points but it seems to presuppose the use of a single model of some arbitrary complexity to examine a system. However, following the discussion in Flather (2000) there seems to be no reason why an assemblage of models cannot be put together to examine a system. The results could be compared one against the other and could provide insights, especially when a simpler model is compared with a considerably more complex one. Many models have been compared in this review. The discussion, however, can be taken further. The actual comparison of one model against another is a complex issue and may be a future modelling challenge. It has been part of the NOMADS2 project, which is a comparison of advection–diffusion models in the North Sea to develop tools to compare rigorously their relative performances. In an ensemble of model results, quantifying the deviation of a particular model from the mean may be difﬁcult. However it could also be the case that the outlier of a group is in fact the closest to reality. The time element also means that under certain conditions particular models may be very close to reality but under other conditions a different set of models may be the closest. On these grounds, therefore, a multiplicity of models is desirable. The difﬁcult challenge is not just the development of the models themselves but also ﬁnding tools that would meaningfully explain why certain models are better at different times. This is a crucial point. As mentioned above, models may move from the academic world into the engineering realm and the comment by Salomon (1994) is salutary, that such models will be used by engineers and a failure of a model could have serious consequences. Using a suite of models would give greater conﬁdence. The suite of models would also be necessary in trying to develop tools that could give overall conﬁdence limits on any particular model.

Is there a trend towards rationalisation? To answer the second question posed in the introduction, there is indeed a trend towards nationally based general models. There is, furthermore, already an example of an international general model with the HIROMB operational model, a collaboration between ﬁve nations (Funquist & Kleine in press). Such models are becoming part of large-scale systems with realistic or even real-time inputs from meteorological agencies that may be coupled with biological, chemical and sedimentary processes. In due course they may become part of the management tools of various agencies. Many of the operational schemes described by Flather (2000) already have such surrounding systems: the POLCOMS system (Fig. 5) (Allen et al. 2001, Holt & James 2001, Holt et al. 2001), the BSH system, MUMM, NORWECOM, the suite of models at WL-Delft Hydraulics, the TELEMAC system, the HAMSOM/ECOHAM1 system, the GHER system, the DHI MIKE21 and MIKE3 (FVM) based systems as well as the ELISE system and others. The COHERENS model is almost a system by itself. Such models could of course become very valuable in providing the background to regional or limited area coastal models. However, the scale of operation 108

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requires due consideration of operational requirements. Some of the systems have to employ massively parallel computing schemes and naturally they require elaborate visualisation methods for the large quantities of data produced (Wolanski et al. 2000). The documentation is also not an insigniﬁcant task, for the COHERENS model the documentation runs to over 900 pages and so electronically based documentation is necessary to enable searches and links. These systems widen the above discussion about the desirability of having different, overlapping, hydrodynamic models. It may now also be desirable to have an overlap between the non-hydrodynamic aspects of these extended systems for similar reasons.

What are the best schemes available? To answer the third question posed in the introduction is impossible and may be impossible to answer for a long time into the future. Apart from actually developing the tools to decide on the “best” model, the responses from the questionnaire indicated that in certain technical matters such as advective schemes or turbulence closure there is certainly no Europe-wide consensus as to the best schemes. One of the main advantages of the European modelling effort is that a forum exists to set up formal projects to compare models or to use models in particular contexts. All the European projects listed in Table 2 (p. 50) have modelling components. An intercomparison is very much part of projects such as ESODAE, NOMADS2, MEDMEX and CARTUM. Formal contacts can lead to more informal model comparisons. For example, Smith et al. (1996) input data from the Norwegian Meteorological Institute to three models from the Institut für Meereskunde, The Norwegian Institute of Marine Research and the Proudman Oceanographic Laboratory. There was reasonable agreement between the models except for the ﬂows through the Irish Sea. Further informal comparison was mentioned in the operational oceanography section (p. 105). Another model intercomparison study was part of the Metocean Modeling Project (MOMOP) (Røed et al. 1995). There have been, of course, other model intercomparisons outside Europe such as the DAMÉE–NAB Data Assimilation and Model Evaluation Experiment–North Atlantic Basin, which compares United States based models (Chassignet et al. 2000). However, because ever more stringent methods of comparing models are being evaluated and developed in such projects as NOMADS2, in the future it may become possible to examine a situation and be able to state categorically that a particular method is the “best” to use. Indeed, an advanced model may be able to select the best approach itself but such a situation is a long way off and an ensemble method may be the most appropriate scheme at present.

Are there any problems not currently being addressed? From the point of view of the need for modelling, as opposed to purely academic interest and scientiﬁc curiosity, the modelling challenges may have to approach what is loosely termed “the public need” and have a directly perceptible effect on the general public. Tides, sea-level changes, including surges, and waves were the originally perceived needs and are now part of operational schemes. Even these relatively simple needs have not reached the end of their modelling development. A greater precision in such models, perhaps coupled with inundation models to identify more precisely the risks in various areas, would be of 109

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public beneﬁt. The next generation of needs are salinity and temperature as they contribute to the density of the water and also inﬂuence the fate of pollutants as well as governing biological activity. The present systems being set up, therefore, are beginning to address these issues but so far have been limited to more general problems. Members of the public sometimes require very direct localised information and may need new model developments. As an example, litter being transported from coastal dumps and/or shipping lanes and being washed up on a beach can be very complex to model if it is subject to wind and wave action as well as general water circulation. It can nevertheless be of prime importance in tourist areas. In conclusion, therefore, the multiplicity of models seen in the European context is deﬁnitely needed, as are the tools for comparison, some of these coming from the unique forum that exists in the European Union. For the foreseeable future, the diversity of models is unlikely to be reduced. This diversity, therefore, may be considered desirable and may indeed indicate the healthy state of modelling in Europe.

Challenges for the future Continuing the theme of a forward look, the following list assembled from a number of sources, is an attempt to state some of the ongoing and future modelling challenges. Although work is proceeding on several of these already, fully comprehensive modelling solutions may be some way off. It may only now be possible to investigate some of the more complex issues using the modelling systems described above. (1) (2) (3)

(4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17)

Alien species, the spread of accidentally or deliberately introduced species. Changes in the ultra-violet balance. Climate change; coastal and shelf seas may be particularly susceptible to climate change, e.g. the sensitivity of the Baltic as reported by Omstedt & Nyberg (1996). Models will help to detect the effects of changing temperatures, rainfall and other factors. The effects of global sea-level change (e.g. altering processes such as the wave climate) (MacDonald & O’Connor 1996). Resultant effects (e.g. sea-level rise changing the wave climate, and hence water turbidity, in the nearshore leading to biological effects). Coastal aquaculture, sources of natural nutrition, the fate of waste products. Coastal zone development and habitat destruction. Deep-sea mining, modelling the mining environment and possible spread of contamination. Dumping directly into the sea. Deep-sea waste disposal, i.e. burial beneath the sea bed and the capping required to prevent seabed erosion and hence release. Litter. Digital elevation models for erosion studies. Oil exploration and exploitation. Remediation of offshore exploitation sites. Overﬁshing. Persistant xenobiotics, e.g. endocrine disrupting chemicals. Radioactivity, i.e. effects of coastal discharge, liberation of radionucleides from sediments, the dumping of radionucleides at sea and possible leakage. 110

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(18) (19) (20) (21)

Sound, the propagation of sound and sources of undersea sound may be subject to density changes. Purposeful tracers and other scientiﬁc interventions. War, terrorism, the release of oil, toxic substances, etc. into the sea, deliberately in locations where most damage will be done. Waste heat from factories and power plants.

Acknowledgements Very great thanks must go to all the modellers across Europe, too numerous to mention, who very kindly ﬁlled in my questionnaire to reveal the state of coastal and shelf-sea modelling in Europe. Many went further and sent copious references, reprints and good wishes which were all very helpful and much appreciated. I would also like to thank those who read portions of my text and made so many helpful comments. Finally, I must acknowledge the support for this review which was provided by several laboratory projects at the Proudman Oceanographic Laboratory as well as the EU- NOMADS2 (MAS3-CT98-0163) project.

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Xing, J. & Davies, A. M. 1996c. The inﬂuence of mixing length formulation and stratiﬁcation upon tidal currents in shallow seas. Estuarine, Coastal and Shelf Science 42, 417– 456. Xing, J. & Davies, A. M. 1996d. A numerical model of the long term ﬂow along the Malin-Hebrides shelf. Journal of Marine Systems 8, 191–218. Xing, J. & Davies, A. M. 1996e. Processes inﬂuencing the internal tide, its higher harmonics, and tidally induced mixing on the Malin-Hebrides shelf. Progress in Oceanography 38, 155–204. Xing, J. & Davies, A. M. 1998a. Inﬂuence of stratiﬁcation upon diurnal tidal currents in shelf edge regions. Journal of Physical Oceanography 28, 1803–1831. Xing, J. & Davies, A. M. 1998b. Formulation of a three-dimensional shelf edge model and its application to internal tide generation. Continental Shelf Research 18, 405– 440. Xing, J. & Davies, A. M. 1998c. A three-dimensional model of internal tides on the Malin-Hebrides shelf and shelf edge. Journal of Geophysical Research 103, 27,821–27,847. Xing, J. & Davies, A. M. 1999a. The effect of wind direction and mixing upon the spreading of a buoyant plume in a non-tidal regime. Continental Shelf Research 19, 1437–1483. Xing, J. & Davies, A. M. 1999b. Processes inﬂuencing the circulation and bottom mixing across the shelf slope: the importance of the slope topography and the coast. Journal of Marine Systems 21, 341–377. Xing, J. & Davies, A. M. 2001a. The inﬂuence of shelf edge ﬂows and wind upon the circulation on the Malin Shelf and in the Irish Sea. Continental Shelf Research 21, 21– 45. Xing, J. & Davies, A. M. 2001b. A three-dimensional baroclinic model of the Irish Sea: formation of the thermal fronts and associated circulation. Journal of Physical Oceanography 31, 94–114. Young, E. F., Aldridge, J. N. & Brown, J. 2000. Development and validation of a three-dimensional curvilinear model for the study of ﬂuxes through the North Channel of the Irish Sea. Continental Shelf Research 20, 997–1035. Young, E. F., Bigg, G. R., Grant, A., Walker, P. & Brown, J. 1998. A modelling study of environmental inﬂuences on bivalve settlement in The Wash, England. Marine Ecology Progress Series 172, 197–214. Zahel, W. 1991. Modeling ocean tides with and without assimilating data. Journal of Geophysical Research 96, 20,379–20,391. Zavatorelli, M., Baretta, J. W., Baretta-Bekker, J. G. & Pinardi, N. 2000. The dynamics of the Adriatic Sea ecosystem. An idealized model study. Deep-Sea Research I 47, 937–970. Zecchetto, S., Umgiesser, G. & Brocchini, M. 1997. Hindcast of a storm surge induced by local real wind ﬁelds in the Venice Lagoon. Continental Shelf Research 17, 1513–1538. Zoppou, C. & Roberts, S. 1999. Discussion of “Space-time conservation method applied to Saint Venant equations”. Journal of Hydraulic Engineering 125, 891–892. Zuur, E. A. H. 1991. The PROSPER general circulation model, an adapted form of the Sandia Ocean modelling method: a numerical description. International Journal for Numerical Methods in Fluids 13, 251–263. Zuur, E. A. H. & Dietrich, D. E. 1990. The SOMS model and its application to Lake Neuchâtel. Aquatic Science 52, 115–129.

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Oceanography and Marine IBiology: an Annual 143–169 BI OGEOCHEM STRY OF ANT AReview R C T I C2002, S E A40,I C E © R. N. Gibson, Margaret Barnes and R. J. A. Atkinson, Editors Taylor & Francis

BIOGEOCHEMISTRY OF ANTARCTIC SEA ICE DAVID N. THOMAS1 & GERHARD S. DIECKMANN2 1 School of Ocean Sciences, University of Wales Bangor, Menai Bridge, Anglesey, LL59 5EY, UK e-mail: [email protected] 2 Alfred Wegener Institute for Polar and Marine Research, Am Handelshafen 12, D-27570 Bremerhaven, Germany

Abstract Antarctic sea ice at its maximum extent in winter covers 40% of the Southern Ocean in a frozen layer, on average, 1 m thick. Sea ice is not solid, rather it is an ice crystal matrix permeated by a labyrinth of brine ﬁlled channels and pores in which life thrives. Organisms are constrained by a set of physicochemical factors quite unlike anything they encounter in the plankton from where they are recruited. Because sea ice is increasingly viewed as a suitable proxy for life in previous periods of the Earth’s history, and even for astrobiology, it is pertinent that the physicochemical constraints acting upon sea-ice biology are better understood. The, largely microbial, network that develops in the ice itself imparts a unique chemistry that inﬂuences the nature and chemical composition of biogenic material released from the ice. This chemistry can result in the export of material to the sediments with distinctive chemical signatures that are useful tools for reconstructing past sea-ice cover of the oceans. This review synthesises information on inorganic nutrient, dissolved organic matter and dissolved gases from a variety of Antarctic ice habitats.

Introduction The ﬁrst published record of the life within Antarctic pack ice was that of Sir Joseph Dalton Hooker in the pioneering voyages of HM Discovery ships erebus and terror in 1839 to 1843 (Hooker 1847). This followed the descriptions of dense growths of diatoms within Arctic pack ice by Ehrenberg (1841). Brown ice, coloured by the rich microbial assemblages it contains was subsequently logged by sealers and whalers who acknowledged higher biological activity in areas of the pack where brown ice was encountered. However, sea ice, which at its maximum extent can cover up to 13% of the Earth’s surface, has mostly been studied by engineers and ship builders interested in constructing ships capable of traversing frozen oceans or platforms that can withstand the pressures of ice ﬁelds. In the past 30 yr there has been a concerted effort to study the biology of pack ice, in particular the ecology of the microbial network that is fundamental to the ecology of seasonally ice-covered oceans and seas. As more has been discovered about these organisms more attention has been devoted to the understanding of the adaptations to their physiology and metabolism that enable them to live within the ice matrix. In recent years this interest has been spurred on by the prospect of discovering products or processes that may be harnessed for novel biotechnologies (Russell 143

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1997, 1998, Nichols et al. 1999a) and lately in the use of sea-ice organisms as proxies for possible life on ice covered moons of Jupiter (Gaidos et al. 1999, Kivelson et al. 2000, Chyba & Hand 2001, McCord et al. 2001) or for previous geologic events in Earth’s history such as Snowball Earth (Hoffman et al. 1998). The organisms that thrive within the ice and the physiology that enables them to exploit this unique habitat have been the subject of several comprehensive reviews, and readers are directed to these for a full understanding of the current knowledge of the biology of sea ice: Horner (1985 a,b), Garrisson (1991), Eicken (1992), Horner et al. (1992), Legendre et al. (1992), Palmisano & Garrison (1993), Ackley & Sullivan (1994), Helmke & Weyland (1995), Kirst & Wiencke (1995), Nichols et al. (1995), Garrison & Mathot (1996), Nichols et al. (1999a), Staley & Gosink (1999), Lizotte (2001), Schnack-Schiel et al. (2001a), Brierley & Thomas (2002), Thomas & Dieckmann (2002). It is beyond the scope of this review to cover this ground again and only where it is pertinent will detailed discussion of sea-ice biology be given. It sufﬁces to say that a complex microbial network can develop within sea-ice biological assemblages. These assemblages tend to be dominated by pennate diatoms, bacteria and protozoans, although the diverse autotrophic and heterotrophic organisms found within the ice, range in sizes from viruses through to small copepods. Despite the physiological and biochemical prerequisites for survival and growth in the low temperatures, low light and high salinities within the ice matrix (Thomas & Dieckmann 2002), there have been surprisingly few endemic microalgal or protozoan species associated with sea ice (Vincent 2000). It is likely, however, that with the advent of modern molecular techniques that many new endemic microbial species will be described (Andreoli et al. 1999, Montresor et al. 1999, Vincent 2000), especially as more bacterial species are isolated and described (Helmke & Weyland 1995, Nichols et al. 1995, 1999a, Staley & Gosink 1999, Sheriden & Brenchley 2000). A particularly exciting discovery was the new dinoﬂagellate species, Polarella glacialis isolated from sea-ice brine in McMurdo Sound (Montresor et al. 1999). The cysts of P. glacialis are remarkably similar to fossil Suessiaceae cysts dating back to the Triassic and Jurassic and, to date, P. glacialis is the only extant member of the Suessiaceae. This review seeks to synthesise the current knowledge of interactions between the biology and chemistry of sea ice. Sea ice provides a unique range of ephemeral habitats for planktonic organisms which during their life in open water are buffered against dramatic changes in their physicochemical environment, with the exception of the light regime and, at times, changes in the availability of inorganic nutrients. When incorporated into the ice these organisms are subjected to very different chemical and physical constraints that vary greatly during the annual cycle of ice formation, consolidation and melt (Eicken 1992). These constraints also change on much shorter timescales of days and even hours because the nature of ice is largely governed by the prevailing air temperature. Superimposed on this highly variable physicochemical environment, is the inﬂuence of the organisms themselves on the chemistry and even the physical nature of the ice. Many parts of the sea ice are effectively isolated from the air or underlying water with dramatic consequences for the transport and diffusion of gases and other dissolved constituents of sea water. Sea-ice organisms also excrete substances that can alter the physical nature of their immediate surroundings, and intense grazing by proto- and metazoans can result in a chemical environment that is highly unusual for the surface layers of the ocean. The organisms trapped within the sea ice are eventually released into the water column when the ice melts, or are consumed by grazers, such as the krill, which exploit the abundant 144

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food resources on the peripheries of ice ﬂoes (reviewed by Daly 1998 and Brierley & Thomas 2002). The physical and chemistry within the ice can result in the biochemical nature of this material being quite different from that of the planktonic forms of the same organisms. This can have considerable consequences for the quality and quantity of the seasonal ﬂux of biogenic material from surface waters and export to the underlying sediments (Fischer et al. 1988, Wefer & Fischer 1991, Leventer 1998, Thomas et al. 2001b). Here the discussion is restricted to what is known of Antarctic sea ice. Several Arctic seaice studies are highly pertinent to this topic but, in general, the two systems are signiﬁcantly different to make their joint treatment unwieldy and not that relevant. Even within the Antarctic sea-ice zone, the range of sea-ice types and the variety of habitats within each of these is staggering.

Sea-ice characteristics As alluded to in the Introduction, the sea ice covering 40% of the Southern Ocean is not a uniform habitat. It is a heterogenous complex of habitat types ranging from loose accumulations of ice crystals a few centimetres thick to heavily deformed pressure ridges and multiyear ice that can be over 10 m thick. The concept of gradients of temperature, salinity and light through an ice ﬂoe (Eicken 1992) are important, because they confer a range of physicochemical conditions which determine the types of organisms and biological activity that takes place at any one horizon in the ice. Sea ice is formed in autumn when katabatic winds sweeping off the Antarctic continent cool the surface of the water. This cooling results in the formation of ice crystals that rise to the surface where they aggregate into dense slicks of grease ice (Fig. 1). It is at this early stage that the ice is “inoculated” with organisms because plankton can stick to, or are “scavenged” by the ice crystals as they rise through the water. Under turbulent conditions the ice crystals consolidate into pancakes (Lange et al. 1989, Wadhams 2000) that grow, aggregate and raft together to form a closed ice cover (Fig. 2). Freezing and the subsequent thickening of the ice take place by congelation ice growth where water molecules freeze onto the bottom of the ice sheet (Wadhams 2000). Most of the Antarctic ice grows to, on average, 1 m thick, although rafting and deformation processes can result in signiﬁcantly thicker ﬂoes being formed. In contrast to the Arctic, 80% of the Southern Ocean pack ice melts during summer, and multiyear ice (ice that persists for more than one growth season) is characteristic of restricted areas such as the northwestern Weddell and Bellingshausen and Amundsen Seas. When sea ice forms under calm conditions the ice crystals, rather than forming pancakes, form uniform sheets of ice. These also thicken by congelation growth (Wadhams 2000). Salt does not enter the ice crystal structure and so, as the ice forms, it and other dissolved constituents of sea water are expelled and collect as a highly concentrated brine solution within the labyrinth of brine channels and pores in the ice matrix (Eicken 1992, Weissenberger et al. 1992, Wadhams 2000). The morphology of these channels and pores, the total volume of the ice occupied by them and the salinity of the brines contained within them is governed by temperature and the age of the ice. As the ice grows and ages there is a continuous loss of brine due to brine expulsion and gravity drainage resulting in a gradual reduction of bulk ice salinity. This loss of brine can be compounded in summer sea ice when melting surface snow and ice on ﬂoe surfaces perculate down, ﬂushing brine out (Wadhams 2000). 145

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Figure 1 Windswept slicks of “grease ice” on the surface of the Weddell Sea in autumn. This is the ﬁrst stage of sea ice formation when frazil ice crystals formed when sea water freezes at –1.8°C accumulate on the surface of the ocean. The prominent slick in the foreground is approximately 50 m wide. (Photograph – D. N. Thomas)

Figure 2 Turbulent water motion cause the frazil ice crystals collected as grease ice (Fig. 1) to coalesce to form “pancake ice”. Initially these very fragile discs are less than 10 cm in diameter (bottom right hand corner of photograph). The size and thickness of the pancakes increases rapidly due to the accumulation of more frazil ice crystals (as illustrated in the photograph – from left to right). (Photograph – D. N. Thomas)

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Figure 3 Dense accumulations of ice platelets can collect underneath land fast ice and pack ice (right). These collections of platelets form dense layers trapping interstitial water between the crystals. Most of the ice-algae and bacteria associated with platelet ice layers are attached to the platelets that have complex surfaces with cracks, crevices and pits (left). (Photographs – D. N. Thomas)

A characteristic of land fast ice, and to a lesser extent pack ice (Smetacek et al. 1992, Grossmann et al. 1996) of coastal regions is the accumulation of layers of platelet ice underneath the sea ice. Ice platelets are discs of ice formed in supercooled water advected from under ice shelves, and can be up to 15 cm in diameter and 3 mm thick (Fig. 3; Dieckmann et al. 1992). These loose platelets can accumulate and trap interstitial water between them in large clouds several metres thick, which eventually can become incorporated into a rigid consolidated platelet layer under the congelation ice (Günther & Dieckmann 1999, 2001). In recent years, surface features of sea ice have received increased attention by glaciologists and biologists alike. These features can range from surface melt ponds to slush ice or seawater ﬁlled gap layers at or below the freeboard of the ice ﬂoe (Garrison & Buck 1991, Ackley & Sullivan 1994, Fritsen et al. 1994, Haas et al. 2001). Although melt ponds are reported from Antarctic summer sea ice (Ackley & Sullivan 1994, Wadhams 2000, SchnackSchiel et al. 2001b), they are not as prevalent as in the Arctic. The gap features and inﬁltration layers of late spring and summer are characterised by highly porous ice, which may allow exchange of sea water. The formation of these surface porous layers is complex, and terminology describing them is often confused. However, recently it has been shown that they are governed by major transformations of snow and ice properties during summer and ﬂood-freeze cycles in winter (Fritsen et al. 1998, Haas et al. 2001). Biological assemblages have been reported from almost all of these habitat types. The brine channel system provides a habitat that is controlled by the conﬁnes of the brine channel and pore diameters and the salinity of the brine (Krembs et al. 2000). Notably, this labyrinth can provide large areas of ice from which grazing organisms are excluded because 147

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Figure 4 Underwater photograph of the edge of an ice ﬂoe, where the water surface is just visible. The ice face was the top of a second-year ice ﬂoe in the Amundsen Sea during late austral summer. The photograph shows dense accumulations of ice diatoms in the surface layers of the ice ﬂoe (darkened regions), as well as vertical brine channels that are also stained by high diatom biomass. (Photograph – D.N. Thomas).

of their size, thereby promoting the establishment of high bacterial and algal standing stocks in the ice (Fig. 4). However, it is within the more porous ice layers such as gap, inﬁltration and freeboard layers and the ice on the periphery of an ice ﬂoe, which is always in contact with water at its freezing point, that the primary production of ice algae and subsequent grazing activity is at its highest. Ice assemblages on the bottom of ice ﬂoes and land fast ice are particularly proliﬁc, reaching algal standing stocks many orders of magnitude greater than in the underlying water (Lizotte & Sullivan 1992, McMinn & Ashworth 1998, McMinn et al. 1999, 2000, Trenerry et al. 2002). This profuse growth is due to the possible exchange of inorganic nutrients with the sea water and a surface on which to grow on. Platelet ice layers can be sites of high biological activity, with most of the algae and bacteria growing attached to the platelets where an unrestricted exchange of nutrients between the interstitial and surrounding water takes place (Arrigo et al. 1995, Grossmann et al. 1996, Riaux-Gobin et al. 2000). The over-riding constraints on the biological activity in the ice are space, temperature, salinity and, for primary production, the quantity and quality of light transmitted through the ice. Sea-ice algae have a high photo-acclimation potential and a physiology well acclimated to low light conditions. The physiology of the dominant ice organisms is also well acclimated to a dynamic salinity regime and can cope with both hyper- and hyposaline stress (Eicken 1992, Kirst & Wiencke 1995, Lizotte 2001, Thomas & Dieckmann 2002). Secondary to this main suite of constraints is the restriction of exchange of dissolved gases, inorganic nutrients and organic matter. 148

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Dissolved gases The limited data on dissolved gases in sea-ice brines collected to date, led Gleitz et al. (1995) to conclude that when there is high primary production, and accumulation of large algal standing stocks in sea ice, the brines are characterised by substantial reductions in total inorganic carbon, exhaustion of CO2 (aq), pH values up to 10 and O2 supersaturation. Similar ﬁndings have also been described for deformed summer sea ice and in platelet ice systems ﬁlling tide cracks in fast ice as well as platelet layers under the ice (Gleitz et al. 1996a, Günther et al. 1999a, Thomas et al. 2001b). Such changes are not only associated with closed or semi-closed ice systems, but ﬁne scale microelectrode studies have demonstrated strong O2 gradients across the diffusive boundary layers associated with bottom ice assemblages with high algal standing stocks (McMinn & Ashworth 1998, McMinn et al. 2000, Kühl et al. 2001, Rysgaard et al. 2001, Trenerry et al. 2001). The equilibrium of dissolved constituents in the brines will rapidly change with changing salinities and low temperatures. It would be anticipated that calcium carbonate precipitates since precipitation occurs just below the freezing point of sea water (Dieckmann et al. 1991). In ﬁeld and laboratory studies conducted to date, however, no evidence for such precipitation in sea ice has been found (Gleitz et al. 1995, 1996a,b, Günther et al. 1999a). These chemical changes in the carbonate system, oxygen concentrations and pH present an unusual suite of factors that, coupled with declining nutrient conditions in the ice, may control the species composition and growth dynamics of algal assemblages in the ice (Gleitz et al. 1995, 1996a,b). In particular, physiological adaptations that sustain photosynthesis at high O2 and low CO2 in a strongly alkaline environment will be a critical prerequisite for survival in the ice. For example, Gleitz et al. (1996b) consider the ability of algae actively to assimilate HCO3− at very low CO2 (aq) concentrations to be a decisive factor for the success of Chaetoceros cf. neogracile, a small diatom common in established sea-ice assemblages (Gleitz & Thomas 1993). Other sea-ice diatoms have been shown to have active carbonconcentrating mechanisms associated with the ability to utilise HCO3− for photosynthesis (Mitchel & Beardall 1996). Although, as Gleitz et al. (1996b) point out, the major competitive advantage of small species may be gained from their favourable surface to volume ratios, outgrowing larger species by their greater capacity to sustain growth at lower nutrient concentrations. The decreases in inorganic carbon and corresponding accumulation of O2 measured in sea-ice systems produce convincing photosynthetic quotients (PQ = mol O2 produced/mol of CO2 ﬁxed) of between 1.0 and 1.4 (Gleitz et al. 1995, Günther et al. 1999a). These PQs indicate that photosynthetic activity and carbon assimilation are the driving forces in determining the dissolved gas composition within the ice. Low CO2 conditions can be experienced by phytoplankton in sea water, but hyperoxic conditions are rare in marine systems (Raven 1991, Raven et al. 1994). It is also possible that toxic photochemical products may accumulate in such environments (Vincent & Roy 1983, Prézelin et al. 1998). These products include substances such as hydrogen peroxide and hydroxyl radicals which can damage nucleic acids, proteins and other cell constituents. Diatoms have been shown to have high activities of antioxidative enzymes, such as catalase, glutathione peroxidase, glutathione reductase, to cope with these potentially damaging conditions (Raven 1991, Raven et al. 1994, Rijstenbil 2001). Activities of these enzymes in diatoms isolated from sea ice are high over the temperature and light ranges in which maximum photosynthetic activity would occur in the ice and, therefore, lead to the maximum build up high oxygen conditions (Schriek 2000). 149

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Also of interest is the use of photochemical products as tracers for snow deposition rates on sea ice. Photochemical production of hydrogen peroxide in the atmosphere is well preserved in the snow that overlies sea ice. Concentrations vary greatly between summer and winter snow (Eicken et al. 1994) and enable its potential use as a temporal tracer for snow deposition. However, the decomposition rates of hydrogen peroxide (half life of order of a day) are signiﬁcantly enhanced in the liquid and gas phases. Oxygen is also a competitive inhibitor of CO2 carboxylation by ribulose-1,5-carboxylase/ oxygenase (RUBISCO), and carbon-concentrating mechanisms (CCM) can ensure that the oxygenase activity of the enzyme is reduced or suppressed, although CCMs are not ubiquitous in marine algae (Raven 1991). Active CCMs have been demonstrated in sea-ice algae (Gleitz et al. 1996b, Mitchel & Beardell 1996) and will be key to the sustaining of carbon assimilation in these hyperoxic conditions. Another factor that will affect the overall carbon metabolism of cells is the likelihood that photorespiration rates will increase signiﬁcantly with increasing irradiance in an environment of low CO2 and high O2 (Raven et al. 1994). The same kind of diffusion limitations that lead to the build up of O2 in the brines will also apply to dimethylsulphide (DMS) which is derived from dimethylsulphoniopropionate (DMSP), an organic osmolyte that acts as a compatible solute in many algal cells (Malin & Kirst 1997). Light, temperature and nutrient supply have all been shown to inﬂuence the production of DMSP but in sea ice salinity is probably the dominant factor that inﬂuences DMSP production by the ice algae. DMSP is synthesised and accumulated in hypersaline conditions and degraded to DMS and acrylic acid when external salinity decreases. DMSP is broken down to DMS through the action of the enzyme DMSP-lyase and as a result of grazing by proto- and metazoans as well as viral infection. In remote ocean regions DMS accounts for most of the non-sea salt sulphate in the atmosphere. The oxidation of DMS in the atmosphere to aerosol particles and cloud condensation nuclei is part of a complex system of localised and global climate control (Malin & Kirst 1997). There have been few studies on DMSP and DMS in Antarctic sea ice to date but those that have been done conﬁrm that DMSP concentrations within the sea ice can be orders of magnitude higher than in the open Southern Ocean or waters underlying the ice (Kirst et al. 1991, DiTullio et al. 1998). Sea-ice physicochemical conditions are strongly conducive to high DMSP concentrations because high salinities, low temperatures and low inorganic nitrogen all induce DMSP production (Malin & Kirst 1997). DMSP in sea-ice algae may have a dual role acting as both an active osmolyte and cryoprotectant at the same time, similar to other solutes such as proline (Kirst & Wiencke 1995). The ecological role and implications in sea-ice microbial assemblages of high DMS and DMSP and another breakdown product of DMSP, acrylic acid, are discussed by Brierley & Thomas (2002). However, it is clear that the consequences of a high localised source of these products have signiﬁcant effects on bacterial activity and grazing, both of which are inhibited and stimulated in laboratory investigations (Wolfe et al. 1997, Wolfe 2000). The production of DMSP in sea ice is not only associated with the well known DMSP producer Phaeocystis spp. but also with diatoms which are not generally considered to be proliﬁc DMS producers in the open ocean (Kirst et al. 1991, DiTullio et al. 1998). There is great variation in the distribution of DMSP in the samples analysed to date, largely reﬂecting the variability in species composition of the assemblages but probably more important is the small-scale spatial heterogeneity of the physical factors that inﬂuence the production of DMSP by ice algae, namely light, salinity and temperature. In particular, the brine channel morphology that determines the extent of grazing activity within the ice (Krembs et al. 150

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2000) will determine the degree of grazing-induced release of DMS. DMSP is cleaved to DMS in slightly alkaline conditions and the shifts of pH up to values of 10 that have been measured in sea-ice brine (Gleitz et al. 1995) may enhance this reaction within the sea-ice habitat. It is certain that the greatest production of DMS is associated with melting ice, when cells containing high concentrations of DMSP, induced by high brine salinities, are released into the sea water with a corresponding reduction in external salinity. Periods of ice ablation are also times when elevated grazing activity in ice edge waters will increase the release of DMS into surface waters and therefore into the atmosphere. DMS is not the only volatile gas to be emitted from marine algae with consequences for atmospheric chemistry. Reactive halogen species signiﬁcantly contribute to the destruction of ozone in the polar stratosphere as well as the underlying troposphere. The origins of these increased halogen concentrations and the spatial and temporal extents of their inﬂuence remains unclear. However, tropospheric air enrichment by reactive bromine species closely associated with sea ice has been observed by satellite (Wagner & Platt 1998). These authors speculate that short term high concentrations of BrO in the troposphere are due to autocatalytic bromine release from sea salts on sea ice rather than the degradation of unstable organic compounds containing halogens. However studies have shown that both Arctic and Antarctic sea-ice algae also produce signiﬁcant quantities of a suite of brominated hydrocarbons including bromoform, dibromomethane, bromochloromethanes and methyl bromide, all of which may be converted photochemically into active forms of bromine (Sturges et al. 1992, 1993). The levels of production evidently have important implications for the chemistry of the polar boundary layers and may be similar in order of magnitude, on a global scale, to the inﬂuence of anthropogenic and macrophyte sources (Sturges et al. 1992). The implications of sea-ice biology therefore extend far beyond its central role in the Southern Ocean ecosystem. High concentrations of organisms in the sea ice produce and release concentrated spurts of volatile gases that may be important in the regulation of local and/or global climate and the seasonal patterns of ozone destruction.

Inorganic nutrients Evidently marine algae and bacteria living below their optimum temperatures for growth become unable to sequester organic and inorganic nutrients with decreasing temperature because of lowered substrate afﬁnity (reviewed by Nedwell 1999 and Pomeroy & Wiebe 2001). In general, higher substrate concentrations are necessary at temperatures near the lower temperature limit of a species, and the inhibition of growth due to the low afﬁnity can be reversed if higher concentrations of the substrate become available to compensate for this low afﬁnity effect. Clearly sea ice is such a low temperature environment with both algae and bacteria living at temperatures well below their optima. Low substrate afﬁnity has been proposed as the reason that available substrates are not utilised even though concentrations in the ice may be very high (Pomeroy & Wiebe 2001). In fact, these authors state that “The one situation in nature where dissolved organic matter may accumulate at least in part because of the lower substrate afﬁnity of heterotrophic bacteria is in sea ice” (see discussion on dissolved organic matter, p. 156). This limited afﬁnity is not only applicable to organic matter but also to inorganic nutrients (Nedwell 1999). Stapleford & Smith (1996), for example, demonstrated that Psuedonitzschia 151

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seriata from Arctic sea ice had a reduced afﬁnity for silicon at low temperature. Reduction in silicate afﬁnity of Antarctic phytoplankton at low temperatures has been demonstrated by Jacques (1983) and indicated by Dieckmann et al. (1991), although other studies have shown that this effect is not always pronounced (Nelson & Tréguer 1992). In a comprehensive study of microalgae and bacteria, although not including sea-ice isolates, Reay et al. (1999) measured a consistent decrease in the speciﬁc afﬁnity for nitrate with decreasing temperatures. This relationship was not the case for ammonium for which the speciﬁc afﬁnity was evidently temperature independent. The differences are attributable to the fact that active uptake processes, as is the case for nitrate, are more inﬂuenced by the changes in membrane structure that occur at low temperatures than passive uptake processes which are involved in ammonia transport into the cells. The concentration gradient of ammonia across the cell is maintained by ammonia protonation within the cell and by the equilibrium between ammonium and ammonia outside the cell (Raven et al. 1992, Reay et al. 1999). These processes therefore have profound effects on which source of inorganic nitrogen is used at low temperatures with far reaching ecological and biogeochemical implications for polar oceans (Nedwell 1999, Reay et al. 1999). Any oceanic warming due to climate change as in interglacial periods, could change the afﬁnity for nutrients, and hence primary production patterns, signiﬁcantly especially in regions such as the Southern Ocean where annual primary production is too low to exhaust the supply of inorganic nutrients (Nedwell & Rutter 1994, Nedwell 1999, Reay et al. 1999). The inorganic nutrients, other than inorganic carbon which was discussed above, have been an important component in sea-ice studies in the past decade. However, it is only in very recent years that a more complete overview of the nutrient status of sea ice has been possible (Table 1). Considering the four main nutrients studied, nitrate, silicate, ammonium and phosphate, it is evident that complete exhaustion of all four has been documented and that nutrient limitation can have signiﬁcant effects on the biochemical composition of sea-ice assemblages (Lizotte & Sullivan 1992, Gleitz & Thomas 1993, Gleitz et al. 1996b, McMinn et al. 1999, Thomas et al. 2001b). These changes are discussed below. A noticeable exception is platelet ice where exchange of water (Dieckmann et al. 1992, Arrigo et al. 1995) resulting in nutrient exhaustion has been recorded on only few occasions (Table 1). Similarly in porous summer sea ice and surface layers such as the gap, inﬁltration and freeboard layers (Ackley & Sullivan 1994, Haas et al. 2001), the high standing stocks of algae often recorded will be supported to a large degree by replenishment and exchange of nutrients from the surrounding sea water (Garrison & Buck 1991, Fritsen et al. 1994, Thomas et al. 1998, Kennedy et al. in press). Nutrient exhaustion does take place, however, as the data presented in Table 1 show. However, such ice features are not continuous and pockets or regions in the ice are cut off from this re-supply resulting in microhabitats within close proximity to each other. Even in a single ice ﬂoe, zones of productivity in inﬁltration layers have been measured that are clearly governed by the degree of re-supply of nutrients (Syvertsen & Kristiansen 1993, Gleitz et al. 1996a). Generally, for example, regions closer to the edges of an ice ﬂoe have a greater potential for water exchange than regions towards the centre of the ﬂoe. Several studies have now shown that nitrate based primary production within various seaice habitats proceeds in general stoichiometric balance and nitrate, phosphate and silicate all decrease with decreasing inorganic carbon and increasing oxygen in the external waters (Gleitz et al. 1995, Günther et al. 1999a). Even after nitrate exhaustion, photosynthetic activity continues and low nitrogen compounds are accumulated (lipids and carbohydrates) resulting in rises in pH and depletion of inorganic carbon that are much greater than can be accounted 152

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Table 1 Summary of the ranges (minimum – maximum) of inorganic nutrients measured in a variety of sea ice habitats. Ice type

Fast Fast Fast Fast

Silicate (µM)

ice ice ice ice

Platelet Platelet Platelet Platelet Platelet Platelet Platelet Platelet

2–82* 6# 1–14 layer layer layer layer layer/cracks layer layer layer

Brine Brine Brine Brine Brine Brine Brine Brine Brine 1st year ice Brine 2nd year ice Bulk Bulk Bulk Bulk Bulk Bulk Bulk

sea sea sea sea sea sea sea

ice ice ice ice ice ice ice

Crackpools Inﬁltration layer Inﬁltration layer/ Frazil ice Surface layer Inﬁltration layer

Nitrate (µM) 0–199* 0–2** 4.4# 1–10

61–79 1–316* 20–55 4–62 18– 49 70–91 23–103 50–65

19–32 28–31* 5–30 0–24 1–23 27–33 11–300 0–25

64–225 16–172 4–146

2–95 1–82 0–59 2–15*** 17–121 1–36 13–96 17–21 37# 12#

27–278 1–100 124–226 150# 140# 2– 4† 0–13 0–65 10–65* 8–24 0–74

0– 4 0–8 0–39 0–30* 0–30 1–36 0–28

Ammonium (µM)

Phosphate (µM)

Reference

1–27*

0–38* <0.1 0.4#

Günther & Dieckmann 2001 Stoecker et al. 1998 Arrigo et al. 1995 Riaux-Gobin et al. 2000

2– 4 2–3* 2–3 0–2 1–12 1–3

Arrigo et al. 1995 Günther & Dieckmann 2001 Grossmann et al. 1996 Thomas et al. 2001b Günther et al. 1999a Dieckmann et al. 1992 Riaux-Gobin et al. 2000 Smetacek et al. 1992

0– 4 5–150 1– 4* 1–9 3–178

0–2 6– 48 1–22 2 6–28 2–11 1–3 <1 5# 4# 1–10† 1–16

0–9 1–8 0– 4 2– 42 0–12 3–7 2# 1#

0–18*

1–6 0– 4 0– 4 0–3*

4–8 0–28

0–8 0–10

9–311 2–75

0–27 0–16 34

0–1 0

4– 64 22– 47

0–18 0–22

0–1

Dieckmann et al. 1991 Dieckmann unpubl. Gleitz et al. 1995 Fritsen et al. 1994 Mock in press Weissenberger 1992 Bartsche 1989 Kristiansen et al. 1992 Garrison et al. 1990 Garrison et al. 1990 Eicken et al. 1991 Thomas et al. 1998 Dieckmann et al. 1991 Gleitz & Thomas 1993 Kennedy et al. in press Guglielmo et al. 2000 Thomas et al. unpubl.

1–15 0–2

Gleitz et al. 1996a Syverstson & Kristiansen 1993 Kristiansen et al. 1992

0–1 2–3

Garrison & Buck 1991 Kristiansen et al. 1998

* normalised to seawater salinity; ** these values are in nitrate + nitrite + ammonium *** these values are in nitrate + nitrite; † these are a range of mean concentrations between cores # these are mean values that were cited in works where no range was given.

for by budgeting of inorganic nitrogen pools (Gleitz et al. 1996a,b, Günther et al. 1999a). During this production there is apparently considerable in situ regeneration and accumulation of these regenerated chemical species. The most striking aspect of the collated information in Table 1 is not so much the instances of nutrient depletion but rather the numerous instances of extraordinarily high concentrations of nutrient species: values of silicate up to 153

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316 µM, nitrate 300 µM, ammonium 150 µM, and phosphate 42 µM. As an approximate guide, typical values of surface waters in the Southern Ocean are of the order of: 70 µM silicate, 30 µM nitrate, <1 µM ammonium and <2 µM phosphate. The possibility for such accumulations of these nutrients, especially the high ammonium and phosphate levels is frequently related to high rates of heterotrophic activity within the ice leading to the production of these products in the ice. Arrigo et al. (1995) consider that such high regeneration rates (phosphate 35 µmol l−1 d−1 and ammonium >31 µmol l−1 d−1) are due to bacteria or protozoan metabolism, a view shared by Grossmann et al. (1996), and explain high phosphate levels in platelet ice systems. Several diatom species also release ammonium at high rates under conditions of excess cellular energy (Lomas & Glibert 2000). It seems reasonable to think that the high ammonium values up to 178 µM are related to the grazing and excretion of metazoans (Arrigo et al. 1995), the numbers of which can reach high values in these high ammonia and phosphate systems (Thomas et al. 1998). It has been argued that absence of grazers leads to the establishment of high algal standing stocks in platelet layers (Smetacek et al. 1992). However, this would appear to be an exception since high numbers of protozoan and metazoan grazers have been found in platelet layers (Arrigo et al. 1995, Archer et al. 1996, Grossmann et al. 1996, Günther et al. 1999b). Günther et al. (1999a), however, provide convincing arguments to support the hypothesis that heterotrophic oxidation of organic matter and the excretion of metabolic end-products is not the primary route that leads to high concentrations of regenerated nutrients in sea-ice systems. Instead, they propose that algal mortality and cell lysis, ampliﬁed by inefﬁcient grazing within assemblages of high algal standing stock, will liberate dissolved matter, including nutrients. These authors invoke, in particular, the concept of inefﬁcient feeding by metazoan grazers resulting in algal cell fragmentation and the ingestion of only a fraction of the cell contents. It does not seem likely that the extraordinarily high concentrations of phosphate and ammonium can be explained by the liberation of these internal pools alone. The most probable explanation is that the pools result from a combination of factors such as those proposed by Günther et al. (1999a) coupled with signiﬁcant nitrogen and phosphorus remineralisation (Arrigo et al. 1995). It is evident from many of the studies that the rates of remineralisation or liberation of internal pools are such that these processes exceed the uptake of these nutrients and lead to their accumulation within the brines and interstitial waters (Table 1). The effect of these high concentrations on the metabolism of bacteria and algae in the ice is interesting and perhaps they partially overcome the effects of the reduced substrate afﬁnity at low temperature discussed above. The effect of high ammonium concentrations as a nitrogen source is curious, especially considering the lack of a substrate afﬁnity effect with reduction in temperature when compared with that of nitrate (Reay et al. 1999). Priscu et al. (1989) showed that nitrate reduction enzymes operate more efﬁciently at low temperature than the transport of nitrate into the cell and that it is the latter that limits nitrogen assimilation in sea-ice algae, in keeping with the low substrate afﬁnity at in situ temperatures. Priscu & Sullivan (1998) show that addition of extra nitrate did not increase protein speciﬁc nitrate reductase (NR) activity but that NR activity was signiﬁcantly suppressed at ammonium concentrations of <1 µM. Evidently ammonium has a major regulatory role on the uptake and assimilation of nitrate and, at the high ammonium concentrations reported here, it is presumed that NR activity will be inhibited. Such ammonium inhibition of nitrate assimilation is well reported for marine phytoplankton (Thompson et al. 1989, Flynn 1991). It is not ammonium itself that inhibits 154

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nitrate uptake but a product of ammonium assimilation that inactivates NR by glutamine produced via the glutamine synthetase pathway (Priscu & Sullivan 1998). Although, as reported by these authors, there are instances where high NR activity is maintained in the presence of high ammonium concentrations. At high concentrations of external ammonium, especially at high pH, there is an increase in the proportion of undissociated ammonia with the result that it will diffuse directly into cells (Flynn 1991, Raven et al. 1992, Reay et al. 1999). Clearly the elevated pHs in sea-ice brines will enhance this effect, and sea ice may be one of the few environments with high external pH where ammonium dominated nitrogen nutrition prevails (cf. Raven et al. 1992). In diatoms, intracellular concentrations of nitrate can accumulate to high concentrations. Ammonium generally does not accumulate, and if intracellular levels of ammonium are excessive, its transport is inactivated (Flynn 1991). In contrast, ﬂagellate species can store high amounts of ammonium within the cells. Consequently, studies into the differences in nitrogen assimilation patterns within the constituents of sea-ice assemblages are called for to determine if differentiation in nitrogen metabolism described for temperate diatoms and ﬂagellates (Lomas & Glibert 2000) also plays a role in determining succession patterns within the ice. Kristiansen et al. (1992, 1998) have shown that nitrate was the main nitrogen source for inﬁltration algae accounting for over 90% of the nitrogen source in spring. During the summer, ammonium became a much more important nitrogen source and accounted for up to 21% of nitrogen assimilation. Uptake of nitrite, urea and amino acid was negligible. The uptake of nitrate was highly dependent on irradiance and inhibited at irradiances frequently encountered by such assemblages near the surfaces of ice ﬂoes. In fact, over a 2-wk period they estimated that nitrate uptake rate was reduced by 13% as a result of photo-inhibition. Priscu et al. (1991) also showed that nitrogen uptake by sea-ice algae responded to irradiance changes but that these changes were not proportional to changes in photosynthetic rates. Their nitrogen uptake models predicted that bottom ice assemblages were always irradiance-limited, whereas the nitrogen uptake of surface assemblages was saturated at low irradiances and frequently photo-inhibited. Ammonium oxidising bacteria were found in sea-ice assemblages by Priscu et al. (1990) and may compete with algae for the ammonium. These authors suggest that attached ammonium oxidisers will beneﬁt most from ammonium released into the sea-ice habitats, especially because attachment seems to induce increased ammonium and nitrite oxidising activity in other marine systems. Studies to investigate such processes in detail are clearly needed if the nitrogen cycling within these unusual systems is to be understood. Diatoms comprise the dominant component of sea-ice assemblages and silicate concentration and its turnover within sea ice is fundamental to their dominance in sea-ice habitats. Furthermore, although silicate is certainly often depleted compared with surface water concentrations, it is rarely exhausted (Table 1), which is quite remarkable because many of the high silicate concentrations recorded are also associated with high standing stocks of diatoms. Uptake of silicate from the external medium is active, and the amount of silica in diatom species can vary signiﬁcantly (Martin-Jézéquel et al. 2000). Growth of diatoms, and by implication siliciﬁcation, is governed by many factors such as temperature, light and other inorganic nutrient demands. Clearly, cell size affects the amount of silica needed but factors such as low temperature can also induce thicker frustules. Limiting levels of trace metals also induce increased siliciﬁcation in some diatom species. Diatoms grown in silicatelimited conditions have less siliciﬁed frustules and structures such as spines may be reduced 155

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(Martin-Jézéquel et al. 2000). Günther & Dieckmann (1999, 2001) related species-speciﬁc uptake rates of silicate, and the changes in silicate supply with time, to the changes in species composition of a fast ice platelet layer system. They concluded that silicate availability controls the species composition while nitrate supply determines maximum standing stock. Key to the dynamics of the silicate pools, and the establishment of high silicate pools, such as those given in Table 1, is the dissolution of the diatom frustules that are protected in healthy cells by an organic layer around the frustule. Bidle & Azam (1999) have shown that bacterial activity can hasten dissolution of diatom frustules by enzymatic degradation of this protective layer. Impurities in the diatom frustule and its surface area are all factors that combine to make species-speciﬁc differences in frustule dissolution, which is also signiﬁcantly altered by temperature, salinity and pH (Barker et al. 1994, Martin-Jézéquel et al. 2000, Greenwood et al. 2001). Grazing activity may increase dissolution rates by damaging the protective coatings of the frustules but mostly by fragmenting the frustules, thereby increasing exposed surface areas. However, the packaging of diatoms into membrane-bound faecal pellets, as is commonly found in sea ice (Buck et al. 1990, Thomas et al. 2001b), will effectively reduce frustule dissolution by considerably reducing the surface area. A highly signiﬁcant factor inﬂuencing the dissolution of frustules in the sea-ice system is pH. Shifts in pH to more alkaline conditions up to values of 10 in highly productive sea ice assemblages (Gleitz & Thomas 1993, Gleitz et al. 1995, 1996a,b) will signiﬁcantly enhance the dissolution rates of frustules. This increased dissolution may be counterbalanced by reduced dissolution rates at low temperatures (Martin-Jézéquel et al. 2000) coupled with a decreased afﬁnity by the diatoms for silicate at low temperature (Stapleford & Smith 1996). Diatom frustules compose a large fraction of the material in Antarctic sediments (Zielinski & Gersonde 1997, Leventer 1998) and the heavy siliciﬁcation of some diatom species such as Fragilariopsis species ensures that frustules are well preserved in the sedimentary record (Smetacek 1999). The importance in the durability of the frustules of these species for paleoenvironmental indicators is discussed in the comprehensive review by Leventer (1998). Two species, in particular, F. curta and F. cylindrus are characteristic of sea ice from all areas of the Southern Ocean sea-ice zone (Leventer 1998, Lizotte 2001) and ice edge regions and, as such, provide useful proxies for the interpretation of past sea-ice distribution.

Dissolved organic matter In their synopsis of the biogeochemical developments of a platelet ice system, Günther et al. (1999a) and Grossmann et al. (1996) stressed that the signiﬁcance of dissolved organic matter (DOM) for ice based productivity and carbon turnover was a major unknown in current concepts of sea-ice functioning. Even though the concept of a well developed sea-ice microbial food web was developed at an early stage (Garrison 1991, Palmisano & Garrison 1993, Garrison & Mathot 1996) and the idea that large DOM resources would fuel high heterotrophic activity in the ice was generally assumed, few measurements of the quantity or composition of DOM in sea ice were actually made. Herborg et al. (2001), Thomas et al. (1998, 2001a,b) and Carlson & Hansell (in press) measured dissolved organic carbon and nitrogen in Antarctic sea ice over various seasons and in different regions of the sea-ice zone including a variety of ice types. Accumulation of DOM within the ice is extreme and concentrations 450-fold greater than that of surface 156

BI OGEOCHEM I STRY OF ANT A R C T I C S E A I C E

waters have been measured (Thomas et al. 2001b, Carlson & Hansell in press). Such an accumulation is clearly indicative of the uncoupling of the DOM resource production and consumption processes. Pomeroy & Wiebe (2001) review this phenomenon and conclude that the reduced substrate afﬁnity of bacteria at low temperatures results in them not being able to utilise the high concentrations of available substrates in the ice. There have been limited investigations into the chemical composition of sea-ice DOM. Some of the earlier work by Arrigo et al. (1995) showed that concentrations of amino acids and sugars accumulated in platelet layers, although the concentrations that they measured were at sub-µM levels, very low compared with the levels of DOC and DON reported above. Comparison of DOC : DON ratios show that the material is often highly carbon enriched with ratios over 50 being common, although the ratios measured are variable and values as low as 3 were recorded (Thomas et al. 2001a). Similar high ratios measured by Thomas et al. (1995) in winter Arctic sea ice were attributed to an uncoupling of the carbon and nitrogen metabolism such that a nitrogen-rich pool of amino acids was hydrolysed faster than carbon-rich polysaccharide pools. Guglielmo et al. (2000) demonstrated signiﬁcant differences in the activities of amino-peptidase and ß-glucosidase in the high algal standing stocks of a Antarctic sea-ice assemblage. These differences led to a preferential turnover of nitrogen enriched organic matter. They estimated that the entire protein pool in the system studied could be “mobilised” on average within 3 h compared with a value of 102 h for the total carbohydrate pool. Despite this trend, it is clear that high amounts of organic matter in the ice do stimulate enhanced glycolytic ectoenzyme activity and that this increased expression may reduce the relative importance of amino-peptidase activity (Misic et al. 1998). Systematic biochemical studies of the hydrolytic enzymes of psychrophilic bacteria are urgently needed for a better understanding of the turnover of organic matter in sea ice. Examples of adapted enzymatic activity are becoming exempliﬁed by a low temperature, high salt-tolerant ß-galactosidase isolated from an Antarctic psychrophile related to species recently found in sea ice (Sheridan & Brenchley 2000). More detailed analyses of sea-ice DOC have shown that it can be formed from up to 99% carbohydrate (Thomas et al. 2001a), although values of 10% to 30% of the DOC pool are more typical (Herborg et al. 2001). In the latter study monocarbohydrates formed a much greater contribution (70–88%) to this pool than did polycarbohydrates over a range of different sea-ice habitats. A somewhat more rigorous characterisation of Arctic sea-ice DOM showed that the material was characterised by high neutral sugar and amino acids with a dominance of glucose and glutamic acid (Amon et al. 2001), which pointed to a fresh nature of the DOM presumed to be produced by ice algae. However, this material was collected from ice containing rather low DOC values (112 µM) and it can be speculated that the high concentrations measured in the Antarctic studies referred to above may have quite different signatures. Many of these signatures clearly result from accumulations of DOM over long periods of time in aged biological assemblages. There is often poor correlation between chlorophyll and DOM and on occasions high DOM concentrations are found where no living ice organisms are present, suggesting that it could be a remnant of past biological activity (Thomas et al. 2001a). Herborg et al. (2001) and Thomas et al. (2001a) have argued that the composition of the DOM within ice should reﬂect largely the chemical composition of the main DOM producers. This argument is based on the fact that much of this DOM is derived from death, break-up and lysis of cells during grazing. Although this is likely to be the case for a high percentage of the DOM in sea ice, algae and bacteria actively excrete matter for a variety of 157

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purposes and this matter will enter the DOM pool. Short-term radio-labelled carbon incubations often show that DOC excretion represents a low percentage of the overall carbon assimilation of ice diatoms (Thomas et al. 1992), although exponentially growing batch cultures of Antarctic diatoms release 10% of the daily photosynthetic carbon assimilated resulting in a substantial build up of DOC (Lara & Thomas 1994, 1995). Krembs & Engel (2001) and Krembs et al. (in press) have shown that in Arctic ice, diatoms can produce copious amounts of extracellular polymeric substances (EPS) within the brine channels. Clearly, a high percentage of the DOM measured will consist of this material. These, mainly polysaccharide, secretions turn the immediate surroundings of sea-ice algae into a viscous medium and signiﬁcantly alter the immediate physicochemical environment. It can be speculated that they may protect ice organisms from ice crystal damage, as well as acting as a buffer against salinity and other chemical stresses. The study of these micro-environments in the ice will be difﬁcult to perform because their integrity is highly dependent on maintaining brine channel morphology. However, it is clear that if such material is widespread within the brine channel matrix, our concepts of nutrient and gas exchange within the brine channel system will be signiﬁcantly altered and it may be more appropriate to consider processes pertinent to marine bioﬁlms (Brierley & Thomas 2002). Sea-ice diatoms also release other organic compounds that may change the physical environment of the ice immediately surrounding them in other ways. These so called “ice active substances” released by a diversity of algal species cause pitting of ice crystal surfaces (Raymond et al. 1994, Raymond 2000). These authors have suggested that the substances may be glycoproteins and the pitting they induce may enhance the optical properties of the ice surrounding the algae, maintain ﬁne brine channel morphology, or enhance adhesion to ice surfaces. This intriguing feature of dissolved organic matter release within the ice needs to be more rigorously tested and to date remains rather speculative. In particular, studies to identify whether or not sea-ice bacteria also release similar substances need to be made, together with a clear characterisation of the substances. The synthesis by Günther et al. (1999a), built on the discussion of Gleitz et al. (1996a), of the changes in biogeochemistry of sea ice during the development of sea-ice algal assemblages is pertinent. High inorganic carbon, low oxygen, low DOM and high nutrients are characteristic of sea ice which, given enough light, will support nitrate-based new primary production. When diffusion and exchange from outside the system is limited this limitation will lead to reductions in DIC and other nutrients and the accumulation of DOM and oxygen that have been discussed above. Grazing and/or high rates of remineralisation will subsequently result in the high ammonia, phosphate and even nitrate and silicate concentrations that are often measured in conjunction with extremely high standing stocks of algae. The liberation of nutrients will promote further growth especially of small algal species with carbon-concentrating mechanisms and enable the efﬁcient use of HCO −3. The comparison to self-perpetuating chemostats in the ice, although a rather simplistic analogy, does not seem altogether unreasonable (Thomas et al. 1998).

The effects of sea-ice chemistry on chemical composition of ice algae Inorganic nutrients, irradiance and the growth status of algae greatly inﬂuence their biochemical composition and there is a tendency to speculate that algal species inhabiting sea 158

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ice will have a carbon metabolism that is different from algae from less physiologically demanding environments (Priscu et al. 1990, Thomas & Gleitz 1993, Mock & Gradinger 2000). The consequences for the chemical composition of ice algae has received considerable attention, in particular with regard to the production of lipids. However, in synthesising the studies made to date no consistent differences in gross biochemical characteristics and responses to abiotic factors between sea-ice algae and algae from other geographic regions have been identiﬁed (Thomas & Gleitz 1993). There are speciﬁc changes in the lipid composition of membranes due to low temperature acclimation in and maintenance of membrane ﬂuidity in sea-ice algae and bacteria. These will not be covered in this review because this large subject area is discussed in detail elsewhere (Nichols et al. 1995, 1999a,b, 2000, Staley & Gosink 1999, Mock & Kroon, unpubl. data). Conspicuous lipid droplets in sea-ice diatoms are frequently observed (Nichols et al. 1989, Priscu et al. 1990, Gleitz et al. 1996a) and increased lipid production is often accompanied by dynamic changes in lipid class abundances during the development of sea-ice assemblage blooms, which are indicative of the profound changes in the physiology of the algae (Nichols et al. 1989, Fahl & Kattner 1993, McMinn et al. 1999, Mock & Gradinger 2000). The interpretations of these obervations indicate that the overriding process dictating these changes in lipid quantity and quality is a direct response to inorganic nutrient limitation within the ice. Naturally, these processes have important consequences for the quality of sea-ice assemblages as a food source for grazing proto- and metazoans but also for chemotaxonomy and organic geochemistry of algal assemblages and associated sediments (Nichols et al. 1989). In particular, the use of lipid composition as biomarkers for identifying the source of organic matter in sediments will be highly dependent on the changes induced within the sea-ice system (Volkman et al. 1998). Pusceddu et al. (1999) have measured values of up to 68% lipids in the biopolymeric carbon ﬂux collected in sediment traps deployed underneath late summer pack ice in Terra Nova Bay. In a longer term study signiﬁcant seasonal changes in the lipid composition were also measured in material collected by traps deployed at another nearshore site at Signey Island, which was covered by fast ice during winter (Cripps & Clarke 1998). However, it is clear that there are very signiﬁcant species-speciﬁc differences in this response and several sea-ice diatoms do not produce high internal lipid reserves even under conditions of low light, high salinity and nutrient limitation (Priscu et al. 1990, Thomas & Gleitz 1993). Lizotte & Sullivan (1992) point out that species-speciﬁc variation of ice algal biochemical composition is mostly expressed in the lipid fraction. These differences will impede the interpretation of observed metabolic patterns in mixed ﬁeld samples and make the formulation of generalised trends difﬁcult (Thomas & Gleitz 1993). Rather than high lipid production, several studies report a greater allocation of assimilated carbon into the polysaccharide fraction during nutrient depletion and exposure to high external salinities (Palmisano & Sullivan 1985, Gleitz & Kirst 1991, Thomas & Gleitz 1993, Gleitz et al. 1996a). As described above, sea-ice algae have been shown to produce copious amounts of extracellular polymeric substances and it is likely that the production and excretion of this material is linked to the changes in cellular metabolism that have recorded high internal pools of polysaccharide material. Nutrient depletion is one of the many factors that can induce excess polycarbohydrate exudation by algae (Hoagland et al. 1993, Herborg et al. 2001). Furthermore, it cannot be discounted that the methods used for determining the allocation of carbon into various metabolic pools have been biased by retention of extracellular polysaccharide material together with the cells during the usual 159

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ﬁltration onto the glassﬁbre ﬁlters that were commonly used for these studies (Gleitz et al. 1996a). Restricted CO2 exchange with the external sea water, combined with high rates of algal growth result in the depletion of total inorganic carbon concentrations in sea ice. This depletion is accompanied by an enrichment of the stable carbon isotopes 13C in the remaining DIC leading to more positive carbon isotope values of δ13CDIC (Gleitz et al. 1996a, Gibson et al. 1999, Thomas et al. 2001a, Kennedy et al. in press). The 13C enrichment in DIC is, in turn, reﬂected in an enrichment in the stable carbon isotope composition of the particulate material, δ13CPOC, values up to −8‰ having been measured in ﬁeld samples (Fischer 1991, Rau et al. 1991b, Dunbar & Leventer 1992, McMinn et al. 1999, Thomas et al. 2001b, Kennedy et al. in press). In contrast, open water values in the Southern Ocean range from between −21‰ to −30‰ (summarised by Kennedy et al. in press). Several of these studies concluded that a high biomass of algae in the sea ice results in the most 13C enriched values because the degree of limitation of CO2 exchange with the external sea water is maximal. However, the work of Kennedy et al. (in press) has shown that this relationship does not necessary apply, especially because some of the highest algal standing stocks in sea ice are associated with semi-enclosed systems where replenishment of nutrients is only possible to a limited extent. Enrichment of 13C in sediments has been suggested as a proxy for past carbon dioxide concentrations in surface waters (Rau et al. 1989, 1991a). However, the investigations described above have shown that 13C-enriched carbon of sea-ice diatoms is possible in sea ice and may confound any reconstruction of past surface carbon dioxide concentrations in seasonally ice covered regions. If POC enriched in 13C is being produced in sea ice it is reasonable to predict that material sinking out from sea ice may result in 13C-enriched sediments, irrespective of regional surface water variations in the partial pressure of carbon dioxide (Dunbar & Leventer 1992). On the other hand it should be noted that ﬂuxes of 13 C-enriched material from sea ice is not always observed, even during periods when large quantities of ice algae are released from the ice and sink to the underlying sediments, especially under sea ice that persists through the summer when primary production in the ice is at its greatest (Thomas et al. 2001b).

Future perspectives The study of the biogeochemistry of sea ice is a relatively new venture in sea-ice research and has evolved from a combination of the ecological and physiological studies that have prevailed to date. These areas of research will continue to grow but will become increasingly dependent on a better understanding of the chemical environment in which sea-ice organisms grow. It is very clear that this development cannot be tackled without a closer collaboration with geophysicists and glaciologists so that the physical constraints driving the chemical and biological components are understood. The subject at this stage is hampered by several factors, the primary one being sampling techniques to measure in situ rates and activities in what is often a closed system cut off from the atmosphere and the underlying sea water. Most ﬁeld sampling is a compromise and few ﬁeld campaigns are able to collect samples that are unexposed to the atmosphere or contaminated from the surrounding sea water. The modiﬁcation of electrodes and optodes 160

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developed for sediment investigations, which can be deployed with minimal disturbance to the system for in situ determination of key biogeochemical parameters, will be an exciting development in the study of sea-ice biogeochemistry (McMinn & Ashworth 1998, McMinn et al. 2000, Kühl et al. 2001, Rysgaard et al. 2001, Trenerry et al. 2002, Mock et al. in press). Our understanding of brine chemistry at low temperature is not well developed. Much more is understood about the chemistry of hypersaline conditions in warm environment conditions and there is a need for similar investigations to be made at the low temperatures encountered in sea-ice. The biochemical characterisation of the organic matter pools within sea ice is a major requirement for our understanding of the biogeochemistry of sea ice. Coupled with this we need to characterise the enzymes and bacterial activities that lead to the turnover of this material. This area of research has exciting potential for biotechnology applications (Nichols et al. 1995, Sheridan & Brenchley 2000), especially when linked to an increased understanding of the changes that are invoked by sea-ice conditions on lipid metabolism and composition in sea-ice bacteria and algae. The biochemical investigation of reduced substrate afﬁnity of sea-ice organisms (Nedwell 1999) needs to be further linked to measurements of organic and inorganic nutrient pools within the ice. There is a requirement for the study of sea-ice biogeochemistry to move forward and harness biochemical and molecular techniques to ecological and chemical studies in order to unravel the complexity of the biogeochemical cycling within the ice and its implications for biogeochemical cycling in the Southern Ocean. It is difﬁcult to see how these ambitious aims can be resolved without the instigation of time series investigations where the development of biological assemblages and the resulting chemical and biochemical changes are studied encompassing all stages of the process from the establishment of the assemblage on ice formation through to its decomposition or release upon ice melt. Such investigations are logistically difﬁcult but longer term ﬁeld campaigns from land based stations (Stoecker et al. 1998, Günther & Dieckmann 1999, 2001) can be combined with chemostat, or mesocosm experiments using cultured isolates of sea-ice organisms in facilities such as large scale-ice tanks in which realistic ice conditions can be replicated (Eicken et al. 1998, Weissenberger 1998, Haas et al. 1999, Krembs et al. 2001). We have come a long way since the ﬁrst observations of sea-ice biology by Ehrenberg (1841) and Hooker (1847) and we understand much about the ecology of sea-ice zones. The challenge in the next phase of sea-ice research is to consolidate efforts into multidisciplinary campaigns where the physics, chemistry, biochemistry and biology of the sea-ice system are tackled in a co-ordinated manner.

Acknowledgements This work was partly supported by the Alfred Wegener Institute, the Natural Environment Research Council, the joint British Council – DAAD ARC programme, the Nufﬁeld Foundation and the Hanse Institute of Advanced Study. We would like to thank the captains and crews of RV polarstern and the pilots and crew of the wasserthal helicopter service for their help on numerous expeditions. We thank numerous colleagues for their help in our seaice research, in particular: A. Belem, H. Bornemann, H. Eicken, K.-U. Evers, V. Giannelli, M. Gleitz, S. Grossmann, S. Günther, C. Haas, G. Kattner, H. Kennedy, C. Krembs, R. Lara, T. Mock, S. Papademitriou, J. Plötz, J.-L. Tison and S. Schiel. 161

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Oceanography and Marine Biology: an Annual ACCUM ULATI ON OF PHYTODETRI T UReview S O N 2002, T H E 40, S E171–232 A FLOOR © R. N. Gibson, Margaret Barnes and R. J. A. Atkinson, Editors Taylor & Francis

ACCUMULATION AND FATE OF PHYTODETRITUS ON THE SEA FLOOR STACE E. BEAULIEU Mail Stop #9, Applied Ocean Physics and Engineering Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543 USA e-mail: [email protected]

Abstract Phytoplankton blooms sometimes result in the mass sinking of phytodetritus through the water column to the sea ﬂoor. The accumulation of a phytodetrital “ﬂuff ” layer on the sea ﬂoor is an episodic or seasonal event in some marine environments. This review provides a comprehensive list of locations in the world where the accumulation of phytodetritus has been observed on the sea ﬂoor. The microscopic and chemical composition of phytodetritus sampled from the sea ﬂoor at shallow to abyssal depths is also summarised. In addition, this review provides an overview of the mechanisms leading to mass sinking events, rates of accumulation of ﬂuff layers, the impact of phytodetritus on ﬂuxes of dissolved and particulate matter at the sediment/water interface, and the fate of phytodetritus on the sea ﬂoor. More studies are needed to understand the importance of these ephemeral phenomena for the ecology of benthic organisms, benthic–pelagic coupling in the carbon cycle, and the geological record in marine sediments.

Introduction Since the early 1980s, the concept of a slow, gentle rain of refractory particulate organic matter as a food source for deep-sea organisms has been modiﬁed to include sudden downpours, or pulses, of relatively fresh organic aggregates, or marine snow. As described by Jumars et al. (1990): The concept of a steady drizzle of more or less refractory leavings of the water column has had superimposed upon it episodic “downpours” or windfalls of highly labile material that ﬁnd short circuits to the bottom. Under certain conditions, phytoplankton from surface waters may aggregate, settle rapidly through the water column, and accumulate, relatively intact, as phytodetritus on the deep-sea ﬂoor. Many long-term studies using moored sediment traps have revealed episodic or seasonal peaks of particulate ﬂux to the deep sea (Conte et al. 2001). As determined for some temperate coastal environments, these pulses sometimes yield a majority of the annual input of organic carbon to the benthos. However, even in shallow habitats, we know little about the expression of these pulses on the sea ﬂoor. The sea ﬂoor acts as the ultimate sediment trap, yet relatively few researchers have inspected visually or collected the sediment/water interface to determine the accumulation of this ﬂux the on sediment surface. If the quantity of phytodetritus deposited is greater than the benthic community can process, a “ﬂuff ” layer, 171

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distinct from underlying sediment, may develop on the sea ﬂoor. The author’s personal observation of such a ﬂuff layer at 4100-m depth in the northeast Paciﬁc sparked an interest in these ephemeral phenomena. The ﬁrst rigorous deﬁnition of phytodetritus, given by Odum & de la Cruz (1963), referred to particulate material originating from decomposing “vegetal” biomass. Brown & Parsons (1972) restricted their deﬁnition to particulate matter derived from phytoplankton: We call it phytodetritus because it is composed largely of phytoplankton held together in large clumps by bacteria and organic slimes. It does contain some living cells, but it is no longer planktonic. In this review the latter deﬁnition of phytodetritus was adopted, acknowledging that phytodetritus also may contain proto- and zoogenic detritus (Thiel et al. 1988/89, Beaulieu & Smith 1998). Pulses of faecal pellets were excluded, even though they may transport algal cells to the sea ﬂoor (e.g. Graf 1989, Pfannkuche & Lochte 1993). Macrophyte and macroalgal detritus, known to accumulate in submarine canyons (e.g. Vetter & Dayton 1998), and reviewed elsewhere (Wolff 1979), were also excluded. Also for this review, the deﬁnition of ﬂuff layers was restricted to those derived mainly from phytodetritus. Fluff layers on the sea ﬂoor, differentiated by Stolzenbach et al. (1992), can also be composed of faecal pellets (Rhoads 1974) or unconsolidated sediment (Fowler & Knauer 1986, Gehlen et al. 1997). The main goal for this review was to document all of the locations in the world where (and when) the accumulation of phytodetritus has been observed on the sea ﬂoor. As suggested by C. Smith et al. (1996), “Because of the potential inﬂuence of phytodetrital ‘carpets’ on benthic chemistry and biology, it is important to determine which regions of the deep-sea ﬂoor sustain phytodetrital accumulations.” The accumulation of phytodetritus may inﬂuence the ecology of benthic fauna and the efﬁciency of organic matter remineralisation on the sea ﬂoor. In addition to an initial database of locations, the composition of phytodetritus is summarised, and overviews are provided for mechanisms leading to mass sinking events, rates of accumulation on the sea ﬂoor, and pathways for the fate of phytodetritus on the sea ﬂoor. The review concludes with a summary of current research programmes and ideas for future research on phytodetritus. The literature search was comprehensive for deep-sea studies but may have missed some references for coastal studies. A few studies of ﬂuff layers in lakes are also included.

Study sites Fluff layers that derive from settled phytoplankton, ﬁrst reported in the Baltic Sea, have been known in shallow waters for over 60 yr (Graf 1992). About 20 yr ago, Billett et al. (1983) revealed the ﬁrst convincing evidence that the phenomenon occurs in the deep sea. Their ﬁnding was initially surprising because such close coupling between benthic fauna and a pelagic food source was not expected to occur in deep waters. Billett et al. (1983) conducted their study at depths between 1370 m and 4100 m in the Porcupine Seabight in the northeast Atlantic. Rice (1983) reviewed accounts of what may have been phytodetritus (ﬂuff ) sampled in this area during research cruises in the 1800s. However, during the mid1980s, Rice et al. (1986) were skeptical that ﬂuff layers would be observed elsewhere in the 172

ACCUMULATION OF PHYTODETRITUS ON THE SEA FLOOR

90° N

45°N

180°W

135°W

90°W

45°W

0°

45°E

90°E

135°E

180°E

45°S

90°S

Figure 1 Sites (listed in Table 1) where the accumulation of phytodetritus was observed on the sea ﬂoor. For those studies with multiple stations, a single site is plotted except for endpoints of the studies by Mackensen et al. (1993), Riaux-Gobin et al. (1997), de Wilde et al. (1998), and E. Escobar-Briones (pers. comm.), sites B and C of the BENBO study, the north and west sites of the BIGSET Arabian Sea study, and ﬁve sites for the JGOFS equatorial Paciﬁc study. Filled symbols indicate sites with seasonal accumulation of phytodetritus (listed in Table 2, with the addition of four shallow-water sites). Update of Fig. 5 in C. Smith (1994).

deep sea, stating “. . . it has yet to be demonstrated whether the phenomenon occurs over a wide geographic range or is restricted to the general area of the Porcupine Seabight.” We now know that phytodetritus accumulates on the sea ﬂoor at many locations in the shallow and deep ocean. Table 1 lists all locations where the accumulation of phytodetritus on the sea ﬂoor has been observed, and a total of 61 study sites are plotted in Figure 1. The list, probably not comprehensive for shallow waters, includes a total of 10 sites at <100-m depth (eight in the North Atlantic, one in the Mediterranean, and one in the Antarctic). Although the list was restricted to the marine environment, ﬂuff layers are also known in lakes (e.g. Otten et al. 1992, Goedkoop & Johnson 1996, Tallberg 1999). Deep-sea sites were listed for all ocean basins, with most in the Atlantic, followed by the Paciﬁc, Southern, Indian, and Arctic Oceans. Although mainly observed in temperate and polar regions, the largest latitudinal extent (10°) of phytodetritus was observed in the tropics (abyssal equatorial Paciﬁc; C. Smith et al. 1996). The largest areal extent of a ﬂuff layer (50 000 km2) was reported at the northwest European continental slope in the northeast Atlantic at depths between 3400 m and 4500 m (de Wilde et al. 1998). At 10 of the deep-sea locations, the accumulation of phytodetritus on the sea ﬂoor appeared to be a seasonal phenomenon (Table 2). Including shallow-water sites with seasonal observations of phytodetritus, 15 sites (25% of total) are plotted as closed symbols in Figure 1. The seasonality at the deep-sea sites suggests ties to seasonal processes in surface waters (Tyler 1988), including the spring bloom in temperate waters, the austral summer bloom in the Southern Ocean, and blooms due to seasonal upwelling. Interannual variability in the timing and magnitude of these blooms is reﬂected in the interannual variability of observations of phytodetritus on the sea ﬂoor. Episodic blooms, such as red tides, can also 173

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Table 1 Location, date, distribution, and microscopic composition of phytodetritus that accumulated on the sea ﬂoor (sites plotted in Fig. 1). “n.d.” indicates not determined. Numbers in parentheses correspond to references in right hand column. General location

Atlantic Ocean Porcupine Seabight, NE Atlantica b

Study site: Lat, Long

Water depth (m)

Date observed

Concurrent observations

Distribution on sea ﬂoor

(1) (2) (3) (4) 50 N, 13 W; (5) Gollum Channel: 50 19 N, 12 51 W

(1) (2) (3) (4) overall range 1008–4535; (5) 2634 –2855

(2) (3) May 1981, Apr.–July 1982; (3) June–Sept. 1983; (4) May 1984; (5) July 1986

Spring bloom: (2) Apr. 1982; (3) May 1983; (4) Apr. 1984

(2) (3) (4) Dark, patchy layer (see Fig. 2A); (3) “areas of the sea bed were sometimes free of detritus when large quantities were present in both shallower and deeper areas;” (5) “thick, blanketing deposit,” “nestled like a dense liquid” in depressions

(6) (8) (9) Aug. 1989, May–Oct. 1991, Sept. 1992, June–Sept. 1993, June–July 1994, Sept. 1996, May–Aug. 1997, June–Sept. 1998, June–Oct. 1999; (9) calendar for phytodetritus observations their Fig. 3

(10) Sediment traps: 1992–99 maximum ﬂux in mid-summer (∼200 mg m−2 day−1, ∼10 mg POC m−2 day−1)

Porcupine Abyssal Plain (PAP), NE Atlantica

4850 BENGAL, DEEPSEAS station: 48 50 N, 16 30 W

NW European continental slope, NE Atlanticb

OMEX III, E, Porcupine P4, Abyssal A2–A4: from 47 47 N, 10 28 W to 50 04 N, 14 41 W

3400–4500

Aug. 1995; (12) not observed in Aug. 1996 (but above see PAP Sept. 1996)

Bay of Biscay, NE Atlantica

(14) (15) BIOCYAN station G1: 47 31 N, 08 28 W

(14) (15) 2100

(13) (14) June– July 1983; (15) June 1987, not observed in Aug. 1986

174

(6) Aug. 1989: concentrated around mounds and in depressions, May 1991: evenly spread over the bottom; (8) Sept. 1996: “sediment covered with greenish layer,” July 1997: “faint green veneer;” (9) coverage 50–96% in summer 1991, 1993, 1994; no layer, just aggregates in 1997–99 (11) “mucus present in Assumed from every sample, but in different, some cores it occurred synopticallyin patches;” occurring, offshore blooms and not from (12) estimated coverage over spring bloom 50 000 km2

(17) Sediment traps: mass ﬂuxes twice as high in summer 1983 (∼150 mg m−2 day−1) than in summer 1986

(13) “accumulations, variables dans le temps, de detritus, notamment dans des sortes de depression (terriers);” (14) “generally observed in holes and craters of the sea bottom”

ACCUM ULATI ON OF PHYTODETRI T U S O N T H E S E A F L O O R

Thickness of layer

Macroscopic appearance

Microscopic components

Sampling method

References

(1) 1–2 cm; (2) 0.5 cm; (5) up to 30 cm (thickest ever reported)

(4) Grey, sometimes with amorphous aggregates up to 5 mm diameter (see Fig. 3A); (5) “soupy,” “easily disturbed by currents from the submersible’s motors”

(1) (2) (3) (4) Multiple corer; (2) photo transects with epibenthic sledge; (2) (3) (4) time-lapse photos with Bathysnap; (5) observed from submersible cyana

(1) Scottish Marine Biological Association 1982, (2) Billett et al. 1983, (3) Lampitt 1985, (4) Rice et al. 1986, (5) Tudhope & Scofﬁn 1995

n.d.

(6) Aug. 1989: coarse, granular, May 1991: ﬂocculent, brownish– green; (7) Sept. 1996: degraded aggregates, July 1997: “occasional lumps of fresh, gelatinous material”

(1) Planktonic diatoms, coccolithophorids, crustacean carapaces; (2) May 1981: chainforming, planktonic diatoms, very few faecal pellets, July 1982: diatoms, faecal pellets, and gelatinous aggregates with coccolithophorids and dinoﬂagellates; (4) May 1984 at 1008– 2030 m: diatoms (Thalassionema sp., Nitzschia spp., and Chaetoceros spp.), at 4535 m: notably different diatom species n.d.

(6) (7) (8) (9) Multiple corer; (6) photo transects with epibenthic sledge; (6) (9) time-lapse photos with Bathysnap; (8) BOLAS lander

(6) Rice et al. 1994, (7) Gooday & Rathburn 1999, (8) Witbaard et al. 2000, (9) Bett et al. 2001, (10) Lampitt et al. 2001

(12) 0.5 cm to >1 cm

(11) mucous layer, not ﬂocculent, not easily resuspended; (12) gelatinous layer

Box corer

(11) Duineveld et al. 1997, (12) de Wilde et al. 1998

(16) discrete aggregates, not a layer (in their Fig. 1d)

(14) “light agglutinate brown or dark green;” (15) “brown and light ﬂocculate material”

(12) As determined by HPLC: Sta. A3: coccolithophorids, some dinoﬂagellates and green algae, Sta. P4: relatively more diatoms; as determined by SEM: Sta. E: mainly coccolithophorids (Emiliania huxleyi) n.d.

(13) (14) (15) observed from submersible cyana; (16) tube cores with submersible cyana

(13) Sibuet 1984, (14) Sibuet 1985, (15) Cahet et al. 1990, (16) Sibuet et al. 1990, (17) Vangriesheim & Khripounoff 1990

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Table 1

continued

General location

Study site: Lat, Long

Water depth (m)

Date observed

Concurrent observations

Distribution on sea ﬂoor

BIOTRANS, mid-oceanic abyssal plain, NE Atlantica b

BIO-C-FLUX, JGOFS NABE, and BIGSET station: 47 N, 20 W; (23) (27) ∼47 50 N, 19 30 W

4500; overall range 4094–4587

(20) Sept. 1985; (18) (19) (20) (21) (23) July–Aug. 1986; (23) (27) N of BIOTRANS: Aug. 1989; (26) Mar.–Aug. 1992;

(18) spring bloom May 1986; sediment traps: (25) major ﬂux event in Aug. 1989 (200 mg m−2 day−1, 30 mg POC m−2 day−1), mass ﬂux low in early summer 1990 (<100 mg m−2 day−1); (22) no major ﬂux event in Aug. 1989; (26) chl a ﬂux maximum in Mar. 1992 (38 µg m−2 day−1), mass ﬂux maximum in Apr. 1992 (225 mg m−2 day−1) (32) Phytoplankton bloom June 1998

(20) July–Aug. 1986: patchy, concentrated in depressions on sea ﬂoor, 10% coverage; (26) Aug. 1992: ﬂocculent layer “occasionally found on the sediment”

(36) Phaeocystis sp. bloom 2 wks prior to cruise with 6 µg chl a l−1

(35) (36) (37) Accumulated in ripple troughs at slack tide

(23) (24) (26) not observed in Mar. 1985, May 1988 (salp faecal pellets, also observed ∼200 km SE by Auffret et al. 1994), July 1988, Aug. 1989, or June 1990 Rockall Trough, NE Atlantica b

(1) SBMA station: 57 12 N, 10 20 W; (28) off the Hebrides: ∼56 45 N, 09 30 W; (29) (30) (32) (33) BENBO site B: 57 26 N, 15 41 W; site C: 57 06 N, 12 31 W; (31) AMES survey: 59 49 N, 07 22 W

(1) 2900; (28) (31) 700–2000; (29) (30) (32) (33) B: 1100; C: 1920

(1) 1982 or older; (28) May 1995; (29) (32) Aug.– Sept. 1997; (30) (32) (33) July 1998; (31) Summer 1998

Loch Ewe, Scotland

∼57 48 N, 05 36 E

30

North Sea

(35) (36) (37) Site A: 52 39 N, 03 40 E; (38) PROVESS cruise: 52 19 N, 04 18 E

(35) (36) (37) 29; (38) 18

May 1972, “Within a week . . . the top 2 cm or so of the sediment had formed a black sulphide layer” (35) (36) (37) May 1989; (38) May 1998

Øresund, Denmarkb

55 58 N, 12 42 E

28

(28) “large amount” on some cores; (31) coverage up to 100% in some areas, more phytodetritus at deeper stations; (32) photos at C in 1998 showed haze of particles on 1 July, but seabed features became visible again by 16 July with patchy ﬂuff “a mat of organic Sediment traps: material over the abrupt increase in surface of the phytopigment content in Apr. 1972 sediment”

(40) Apr. 1983; n.d. (39) Apr.–May 1984, Apr.–May 1985

176

(39) “sedimented phytoplankton did not completely cover the bottom,” “macroinvertebrate activity may contribute to a patchy distribution”

ACCUMULATION OF PHYTODETRITUS ON THE SEA FLOOR

Thickness of layer

Macroscopic appearance

Microscopic components

Sampling method

References

(20) (21) July– Aug. 1986: layer up to 2 cm with discrete lumps up to 1 ml; (27) Aug. 1989: 1–1.5 cm; (26) Mar. 1992: discrete aggregates, not a layer

(19) (20) July–Aug. 1986: colour greenish to whitish and related to degree of degradation; (21) July–Aug. 1986: “slimy,” “dark greenish gelatinous aggregates” could be handled with forceps, sometimes embedded in grey, ﬂuffy, strongly degraded detritus layer; (27) Aug. 1989: discrete, greenish-brown layer; (26) Mar. 1992: greenish, Aug. 1992: whitish, degraded

(20) Sept. 1985: coccolith debris, skeletal remains of radiolarians and diatoms; (19) (21) July–Aug. 1986: abundant coccolithophorids, phaeodarian radiolarians, small faecal pellets (minipellets), and cyanobacteria, more degraded deposits composed of diatoms

Multiple corer; (20) (21) (23) box corer; (20) photo transects with Fotosledge; (21) suprabenthic net attached to Fotosledge

(18) Gooday 1988, (19) Lochte & Turley 1988, (20) Thiel et al. 1988/89, (21) Riemann 1989, (22) Honjo & Manganini 1993, (23) Pfannkuche 1993, (24) Pfannkuche & Lochte 1993, (25) Newton et al. 1994, (26) Pfannkuche et al. 1999, (27) O. Pfannkuche, pers. comm. 2001.

(1) 1–2 cm; (29) At C in 1997: up to 6 cm; (32) At C in 1998: thin (mm) layer in Apr., up to 6 cm in early July, 2 cm in late July; (31) up to 6 cm

(31) “readily resuspended;” (32) darker brown than underlying sediment (see Fig. 3B)

(1) same description as for Porcupine Seabight; (29) (32) SEM photos indicated coccolithophorids (see Fig. 4A)

Multiple corer; (31) photo and video surveys with epibenthic sledge and WASP towed platform; (32) “bed-hop” camera

(1) Scottish Marine Biological Association 1982, (28) Mitchell et al. 1997, (29) Black 1998, (30) Good et al. 2000, (31) Bett 2001, (32) Black 2001, (33) Kennedy & Thomas unpubl. data

n.d.

n.d.

Diatoms (Chaetoceros socialis)

Cores inserted by divers

(34) Davies 1975

(38) 1–3 cm

n.d.

(35) (36) (37) Phaeocystis sp.; (38) diatoms (Paralia sulcata)

(36) (37) Time-lapse photos with STABLE; (38) multiple corer, observed by divers

(39) Up to 1.5 cm; (40) may be bioturbated zone rather than ﬂuff layer

(39) Brownish-green layer, “ﬂuid zone;” (40) “olive-green colour . . . changed into subsurface grey colour after 1–2 days”

(40) Diatoms

Haps corer

(35) Jago et al. 1993, (36) Jago & Jones 1998, (37) Williams et al. 1998, (38) C. Grenz, pers. comm. 2001 (39) Christensen & Kanneworff 1986, (40) Kanneworff & Christensen 1986

177

STACE E. BEAULIEU

Table 1

continued

General location

Study site: Lat, Long

Water depth (m)

Date observed

Concurrent observations

Distribution on sea ﬂoor

Baltic Seab

(41) (42) (43) (44) (46) Kiel Bight: 54 23 N, 10 03 E; (45) Pomeranian Bight: 54 36 N, 13 46 E

(41) (44) (45) (46)

(41) (42) 1980: diatom bloom Mar., sedimented Mar.–Apr.; (44) 1983: diatom bloom Nov., sedimented Nov.–Dec.; (46) similar observations to (41) and (44) above

(46) Shallower stations: patches later transported to deeper parts of Eckernfoerde Bay, deeper stations: ﬂuff carpets, no ﬂuff at freshwater seep sites

Iceland Basin, NE Atlanticb

61 N, 20 W

2400

(41) (43) Mar.–Apr. 1980 (carpet visible on sea ﬂoor for one week); (44) Dec. 1983; (45) several observations Oct. 1996–Dec. 1998, not observed in Mar. 1998; (46) several observations Mar. 1998–Sept. 2000 July 1991

(48) Presence/absence of ﬂuff noted for core samples

Nova Scotian Rise, NW Atlantic Continental slope SE of Georges Bank, NW Atlantic

HEBBLE: 40 27 N, 62 19 W

4830

July 1982

(47) Coccolithophorid bloom June 1991, diatom bloom earlier in the year n.d.

∼41 N, 66 20 W; E of Lydonia Canyon: ∼40 30 N, 67 30 W

500–2400; E of Lydonia Canyon: 2200

n.d.

Ranged from small patches to very heavy cover along 21-km transect; E of Lydonia Canyon: n.d.

Continental slope, NW Atlanticb

DOS 1: 39 45 N, 70 40 W; (51) I. Sta.: 39 05 N, 70 10 W

DOS 1: (51) 1500; (52) 1800; (51) I. Sta.: 2700

Apr.–May 1985, not observed in Nov. 1984; E of Lydonia Canyon: Apr. 1986, not observed in Nov. 1984 or Apr. 1985 (51) DOS 1: July 1976, I. Sta.: Aug. 1976

n.d.

n.d.

Long Island Sound New York Bight

∼41 N, 73 W

n.d.

Mar. 1985

n.d.

n.d.

∼40 N, 73 40 W

<50

July–Aug. 1976

“ﬂocculent material . . . distributed throughout the impacted area”

Continental slope off Cape Fear, NW Atlantic Bermuda Rise, central N Atlantic Continental margin, SW Gulf of Mexico

32 55 N, 76 31 W

850

May, Aug. 1994, Oct. 1995

Bloom Mar.–June, aggregated “in a layer several meters thick at the thermocline” in June, bottom water oxygen depletion over 13 000 km2 in Sept. n.d.

33 42 N, 57 37 W

4418

Oct. 1999

n.d.

n.d.

Sites ranged from 20 40 N, 92 45 W to 22 09 N, 97 21 W

∼200

At speciﬁc sites in 1990, 1997, 1998, and July 1999

n.d.

n.d.

(43) 21; 18; 26; 10–25

178

Accumulated in burrows

(56) “not always present”

ACCUMULATION OF PHYTODETRITUS ON THE SEA FLOOR

Thickness of layer

Macroscopic appearance

Microscopic components

Sampling method

References

(45) mm to cm; (46) up to several cm

(41) (46) Green-brown layer; (43) “loose green carpet;” (45) “ﬂuffy layer is a discrete phase”

(41) Diatoms (Detonula confervacea); (43) (44) (45) diatoms

(41) Cores inserted by divers; (44) (46) box corer; (45) siphoned by divers or with Hyball ROV; (46) video observation

(41) Graf et al. 1982, (42) Peinert et al. 1982, (43) Smetacek 1983, (44) Czytrich et al. 1986, (45) Löfﬂer et al. 2000, (46) P. Linke & E. Sauter, pers. comm. 2001

n.d.

n.d.

Coccolithophorids (Emiliania huxleyi)

Multiple corer

(47) Conte et al. 1995, (48) M. Conte, pers. comm. 2001

Filled burrows to 5 cm

Anoxic (black and smelling of H2S)

Diatoms (Thalassiosira hyalina)

Box corer

(49) Aller & Aller 1986

n.d.

Greenish, “large, ﬂocculent particles,” slightly darker than underlying sediment

n.d.

Camera sled transects

(50) Hecker 1990

n.d.

(51) “very looselypacked, easily resuspended”

(52) “A phytoplankton bloom is probably the source of organic material . . . in the vicinity of DOS 1”

(51) Lee et al. 1979, (52) Grassle & MorsePorteous 1987

1 cm

“porous interfacial ‘ﬂuff ’ layer” Colour changed from yellowish to brown in days, degraded black ﬂoc persisted through Aug.

“detrital products of the spring plankton bloom” Dinoﬂagellates (Ceratium tripos)

DOS 1: (51) Sphincter corer; (52) observed from submersible alvin; (51) I. Sta.: box corer with submersible alvin Sediment proﬁle camera n.d.

Discrete aggregates, not a layer

(56) “green globs”

n.d.

Cores with johnson sealink submersible

(55) Levin et al. 1999, (56) L. Levin, pers. comm. 2001

n.d.

n.d.

n.d.

Multiple corer

(57) L. Keigwin, pers. comm. 2001

n.d.

n.d.

Diatoms, faecal pellets

Box corer

(58) E. EscobarBriones, pers. comm. 2001

>1 cm

179

(53) Stolzenbach et al. 1992 (54) Mahoney & Steimle 1979

STACE E. BEAULIEU

continued

Table 1 General location

Study site: Lat, Long

Mediterranean Sea MEDIMAR-2 Gulf of Lions, cruise: 42 22 N, W 03 55 E Mediterranean Seab

Water depth (m)

Date observed

Concurrent observations

Distribution on sea ﬂoor

1584

Apr. 1991

(60) Phytoplankton biomass (same species as biodeposit) up to 2.7 µg chl a l−1 at 25 m depth with no vertical stratiﬁcation of water column (61) phytoplankton bloom end of winter (e.g. Jan. 1993)

(60) Layer not observed at other stations on same cruise

Maliakos Gulf, Aegean Sea

38 50 N, 22 41 E

7–23

(63) Jan.–Feb. in several years from 1992–2000; (62) present on sea ﬂoor for 10 days

Black Seab

Sta. BSK-2: 43 05 N, 34 E (also collected at other stations)

2200 (also collected at other depths)

“permanent,” but sampled in Apr.–May 1988

(66) sediment trap at n.d. Sta. BSC: faecal pellets packed with diatoms and silicoﬂagellates in spring 1988

∼71 N, 12 W

n.d.

June–July 1989

n.d.

n.d.

20 17 S, 70 28 W

850

Mar. 2000

n.d.

n.d.

∼25 30 N, 112 W

n.d.

n.d.

n.d.

n.d.

31 31 N, 120 14 W

(68) 3800; (69) 3960

(68) Aug. 1991; (69) Apr. 1992

n.d.

n.d.

34 50 N, 123 W

4100

(75) layer: summer/ fall 1990, 1991, 1994, 1996; no layer, just discrete aggregates: 1992, 1993, 1995

(73) (77) 1989– 1998: Bakun upwelling index high in years when layer observed; (70) (71) (73) (77) Sediment traps: maximum POC ﬂux (mg C m−2 days−1) 1990: 17, 1991: 28, 1993: 26, 1994: clogged, 1992 and 1995: <12

40 22 N, 125 13 W

2800

Aug. 2000

n.d.

(71) Detrital aggregates covered up to 1.5% in July 1990; (74) (75) Aug. 1994: up to 100% coverage with “ﬂoc” type; (74) Sept. 1994: “sea ﬂoor had a mottled appearance with a thin cover of ﬂocculent material and clusters of . . . aggregates . . . (‘rad patch’ type)” (see Fig. 2C); (76) detrital aggregates covered up to 4.9% in Oct. 1994 Very similar to “rad patch” aggregates described above for Sta. M

Arctic Ocean GreenlandNorwegian Sea Paciﬁc Ocean Continental margin off Iquique, Chile, SE Paciﬁc Guaymas Basin, Gulf of California Continental slope off Baja California, NE Paciﬁc Sta. M, 220 km W of California, NE Paciﬁca b

Continental slope off Cape Mendocino, NE Paciﬁc

180

n.d.

ACCUM ULATI ON OF PHYTODETRI T U S O N T H E S E A F L O O R

Thickness of layer

Macroscopic appearance

Microscopic components

Sampling method

References

0.2–0.3 cm

(59) “thin layer of bronze-coloured particulate material;” (60) “bioclastic greenish layer,” “unusual brownish biodeposit”

Multiple corer

(59) Riaux-Gobin & Descolas-Gros 1992, (60) Riaux-Gobin et al. 1995

(63) 0.1–0.3 cm

(63) “Golden/brown, ﬂuffy, and slimy upper layer”

Planktonic diatoms (autoﬂuorescent, encysted Chaetoceros spp. and Ditylum brightwellii), coccolithophorids, calcareous dinoﬂagellates, very few faecal pellets (see Fig. 4B) (63) Diatoms (including Coscinodiscus centralis)

Cores inserted by divers

2 cm

(65) “mottled grey-black/ grey-green with jelly-like consistency;” (66) gelatinous

Diatoms (Rhizosolenia sp.) and silicoﬂagellate skeletons; (66) coccoliths (derived from Emiliania huxleyi) and clay particles

Box corer

(61) Kormas et al. 1997, (62) T. Hall, pers. comm. 2000, (63) K. Kormas, pers. comm. 2001 (64) Beier et al. 1991, (65) Pilskaln 1991, (66) Pilskaln & Pike 2001

1–2 cm

Fluffy

Diatoms and dinoﬂagellates

Multiple corer

(67) Graf et al. 1995

1 cm

“ﬂuffy green/brown fuzz”

n.d.

Multiple corer

(56) L. Levin, pers. comm. 2001

n.d.

Green sediment surface

n.d.

Observed and cored from submersible

(69) Discrete aggregates, not a layer

Greenish; (69) clumps 0.5–1 cm in diameter

n.d.

Multiple corer

(58) E. EscobarBriones, pers. comm. 2001 (68) C. Smith 1994, (69) C. Smith, pers. comm. 2001

(74) (75) Aug. 1994: 1 cm (see Fig. 3C); (74) Sept. 1994: 2 cm thick in depressions on sea bed

(71) Detrital aggregates deﬁned as “clumps of amorphous-looking particulate matter distinguishable in photographs by colour, shading, or texture;” (74) Aug. 1994: “light, diffuse, ﬂocculent,” Sept. 1994: “loosely cohesive, golfball-sized”

(74) Chain-forming, planktonic diatoms (Chaetoceros spp. and Rhizosolenia spp.) and phaeodarian radiolarians (see Fig. 4C); abundance of taxa in their Tables 3 and 4

(71) (74) (75) Timelapse photos with camera tripod; (72) (74) (76) tube cores with submersible alvin; (75) camera sled transects

(70) K. Smith et al. 1992, (71) K. Smith et al. 1994, (72) Lauerman et al. 1997, (73) Baldwin et al. 1998, (74) Beaulieu & Smith 1998, (75) Lauerman & Kaufmann 1998, (76) K. Smith et al. 1998, (77) K. Smith et al. 2001

n.d.

n.d.

n.d.

Observed and sampled with ROV

(78) S. Goffredi & D. Stakes, pers. comm. 2001

181

STACE E. BEAULIEU

Table 1

continued

General location

Study site: Lat, Long

Water depth (m)

Date observed

Concurrent observations

Distribution on sea ﬂoor

Volcano 7 seamount, E tropical Paciﬁc

13 23 N, 102 27 W

730–780

June 1984, Nov. 1988

Sediment traps collected organisms common in surface waters in Nov. 1988

Mid-oceanic abyssal plain, E central equatorial Paciﬁc Mid-oceanic abyssal plain, central equatorial Paciﬁcb

Site CD: 04 N, 136 01 W

4469

Mar. 1977

n.d.

On the upper summit: “visible on rocks and sediments,” on the lower summit: occurred in depressions n.d.

(68) (83) (84) JGOFS EqPac stations: 05 S to 05 N, 140 W

(68) (83) (84) 4200– 4500

(68) (83) (84) Nov.–Dec. 1992

(83) Coverage up to 95% (see Fig. 2B) with denser clumps or aggregates >1 cm diameter, variability among cores from multiple corer cast; (68) (83) accumulated in biogenic pits

Magellan Rise seamount, W central equatorial Paciﬁcb

07 04 N, 176 49 W

3150

Mar. 1987

(81) Massive accumulation of diatoms (Rhizosolenia sp.) in surface waters late Aug. 1992 (up to 29 mg chl a m−3), convergence zone intensiﬁed by tropical instability waves Aug.– Sept. 1992; (82) sediment traps: maximum mass ﬂuxes in Aug. 1992, “second conspicuous peak” in biogenic silica during ﬁrst half Oct. 1992 n.d.

Sagami Bay, Japan, NW Paciﬁca b

(87) Observatory: 35 N, 139 14 E

1117–1450; (88) seasonal sampling at 1420

(86) Mar.–May 1991–1994; (87) every spring since 1994, speciﬁcally Apr.–June 1997, Apr.–May 1998

BIGSET WAST: 16 13 N, 60 16 E; (90) NAST: 20 N, 65 35 E

WAST: 4050; (90) NAST: 3190

(90) Mar. 1995, Feb. 1998

(89) Productivity peaked in earlymid Mar. 1995, sediment trap mass and POC ﬂuxes peaked in Mar. 1995 (∼250 mg m−2 day−1, ∼25 mg C m−2 day−1); (91) sediment trap at WAST from 1986– 1995: generally, a secondary maximum in POC ﬂux in Mar. (∼10 mg C m−2 day−1)

(90) Layer only at WAST, variability among cores from multiple corer cast

Indian Ocean W and N Arabian Seaa

182

“thin layer . . . practically covers the sediment surface” on the seamount summit, “troughs were nearly always ﬁlled with a layer of dark debris” (87) “thickness . . . (87) 1997: spring changed on a scale of diatom bloom centimetres from place Mar.–Apr., dinoﬂagellate bloom to place and also day May, 1998: sediment by day” trap maximum mass ﬂux end Feb. (2.5 g m−2 day−1)

ACCUM ULATI ON OF PHYTODETRI T U S O N T H E S E A F L O O R

Thickness of layer

Macroscopic appearance

Microscopic components

Sampling method

References

n.d.

“dark green or green-grey ﬂocculent material”

n.d.

Tube cores with submersible alvin

(79) Wishner et al. 1990

Discrete aggregates, not a layer

“dark globs,” “ﬂuffy and organic”

n.d.

Time-lapse camera on BOM

(80) Gardner et al. 1984

(83) ∼0.5 cm

(83) Green, ﬂocculent

(68) (83) Intact, sometimes autoﬂuorescent diatoms (including Rhizosolenia sp.) and microalgae, very few faecal pellets

(68) (83) Multiple and box corer equipped with camera; (83) camera sled transects

(68) C. Smith 1994, (81) Yoder et al. 1994, (82) Honjo et al. 1995, (83) C. Smith et al. 1996, (84) C. Smith et al. 1997

“Centimetres thick in and around biogenic sediment structures”

“greyish,” “benthic ﬂoc” rather than fresh phytodetritus

Radiolarians (including phaeodarians), diatoms (25–30% Thalassionema/ Thalassiothrix group)

Tube cores with submersible alvin

(85) Reimers & Wakeﬁeld 1989

(87) May 1997: up to 1 cm

(86) Light-green, “soupy,” “easily collapses and mixes with underlying sediments during subsampling”

(87) May 1997: diatoms, coccolithophorids, and dinoﬂagellates

Multiple corer, tube cores with submersible shinkai 2000; (87) video from real-time seaﬂoor observatory

(86) Ohga & Kitazato 1997, (87) Kitazato et al. 2000, (88) H. Kitazato, pers. comm. 2001

(90) 1–2 cm

(90) “discrete layer of brownish ﬂuffy material” or “isolated ﬂocculent aggregates of browngreyish colour 1–20 mm in diameter”

n.d.

(90) Multiple corer

(89) Honjo et al. 1999, (90) Pfannkuche et al. 2000, (91) Rixen et al. 2000

183

STACE E. BEAULIEU

Table 1

continued

General location

Study site: Lat, Long

Water depth (m)

Date observed

Concurrent observations

Owen Basin, Oman Margin, Arabian Sea

(56) 18 19 N, 59 E

(56) 3400

(56) Oct.–Nov. 1994

(89) Sediment trap (56) “present as a deployed Nov. 1994: uniform layer in all ﬁrst cup mass ﬂux cores from this site” 125 mg m−2 day−1

ANTARES I cruise: from 55 02 S, 71 48 E (thickest layer) to 49 S, 58 E

3615– 4748

Apr. 1993

n.d.

Atlantic sector of Southern Oceanb

(95) 45 37 S, 09 37 E; 50 09 S, 05 45 E

(95) 4507; 3717 (95) Cruises ANT– VIII/1–3 in 1989; (94) (95) Apr. 1991

Continental shelf and slope, E Weddell Seaa

(100) Observed at sites ranging over 2000 km, thickest layer at 70 S, 05 E; (99) Trough off Kapp Norwegia: 71 35 S, 12 30 W

(97) (100) Overall range 140– 4000; (99) Trough off Kapp Norwegia: 600

Continental shelf, W Antarctic Peninsula

From 64 12 S, 65 22 W to 66 52 S, 69 06 W

550–673

Paciﬁc sector of Southern Oceanb

U.S. JGOFS AESOPS Sta. MS4: 63 09 S, 170 W; (103) (104) also at 61 52 S, 170 W and 64 12 S, 170 05 W (108) 76 06 S, 174 W; 76 30 S, 173 E; 77 S, 173 E

2885

(105) 700; (108) 550–600

(105) 1995 or older; (108) Dec. 1996, Nov. 1998

∼77 50 S, 166 42 E

Shallow depths

Mar. 1977

Southern Ocean Indian sector of Southern Oceanb

Ross Seaa

McMurdo Sound a b

Distribution on sea ﬂoor

Consistent ﬂuff layer at stations in the Permanently Open Ocean Zone (POOZ), very thin layer or absent north of Polar Front

(95) Observed at stations south of Polar Front, variability among cores from multiple corer cast (100) Austral (100) Observed >25% (98) Phytoplankton summer 1985, 1988, bloom Jan.–Mar. coverage at some 1989, 1991; 1991 with maximum stations during each of (99) Trough off the cruises, 3 stations 4 µg chl a l−1 Kapp Norwegia: at 500–900 m with Feb. 1996, “evidence nearly 100% coverage; of at least one, (99) Trough off Kapp but probably Norwegia: “seems to repeated massive be limited to the very sedimentation bottom of the trough” events” Feb.–Mar. 2001, time-lapse photos showed gradual accumulation over weeks; layer not observed in Dec. 1999–Mar. 2000 (103) (104) Mar. 1998

also see Table 2. also see Table 3.

184

n.d.

Sediment trap ﬂux much greater in austral summer 2000–2001 than in 1999–2000

(102) Seasonal ice cover through Dec. 1997, sediment trap mass ﬂux increasing in Jan. 1998 (when traps recovered) (105) Sampled after combined Phaeocystis sp. and diatom bloom; (107) phytoplankton bloom Nov. 1996

n.d.

“Essentially continuous cover . . . with occasional bare patches grazed by holothurians” (see Fig. 2D) n.d.

n.d.

“dense accumulations”

ACCUMULATION OF PHYTODETRITUS ON THE SEA FLOOR

Thickness of layer

Macroscopic appearance

Microscopic components

Sampling method

References

(56) 0.5 cm

(56) “green ﬂuff ”

n.d.

(56) Multiple corer

(89) Honjo et al. 1999, (56) L. Levin, pers. comm. 2001

(92) Up to 5 cm

(92) “very distinct layer,” “more or less heterogeneous, with translucent globules,” “beige, with browngreenish small clumps” (see Fig. 3D)

Multiple corer

(92) Riaux-Gobin et al. 1997, (93) Pinturier-Geiss et al. 2001

(95) Up to 0.5 cm

(95) “ﬂuff . . . easily recognisable by its yellow-greenish colour”

(92) Planktonic diatoms (Fragilariopsis kerguelensis, Chaetoceros spp., Corethron sp., and Rhizosolenia sp.), some coccolithophorids and silicoﬂagellates, faecal pellets absent or degraded (see Fig. 4D); revival tests yielded diatom assemblages dominated by Chaetoceros spp. n.d.

Multiple corer

(94) Mackensen et al. 1992, (95) Mackensen et al. 1993

(97) 0.1–0.2 cm; (100) 1–2 cm; (99) Trough off Kapp Norwegia: 25 cm

(97) “ﬂocculent brownish or greyish surface layer;” (100) greenish, ﬂocculent in their Fig. 3a; (99) Trough off Kapp Norwegia: “Two kinds of ﬂuff . . . a light greenish kind with a very coherent, almost ﬁberlike structure and a more grey variant with a more grainy consistency” n.d.

(97) “intact, chainforming, centric diatoms and colonies of Phaeocystis,” diatom frustrules in “faecal material, presumably derived from euphausiids;” (99) Trough off Kapp Norwegia: diatoms (almost exclusively Corethron criophilum) n.d.

(96) (97) Multiple corer; (99) box corer; (100) photos from camera triggered by bottom contact

(96) Bathmann 1992, (97) Riemann 1992, (98) Gleitz et al. 1994, (99) Barthel 1997, (100) Gutt et al. 1998

Time-lapse camera tripod, towed video camera, multiple corer (megacorer)

(69) C. Smith, pers. comm. 2001

(101) Up to 2 cm

(103) “ﬂuff layer . . . clearly visible”

(101) Diatoms (Rhizosolenia sp.)

(101) (104) Multiple corer

(105) “thick layer”

(106) “green and smelly”

(105) Phaeocystis sp.

(105) Multiple corer; (108) Haps corer

n.d.

n.d.

“presumably Phaeocystis”

Observed by divers

(101) F. Sayles, pers. comm. 1999, (102) Honjo et al. 2000, (103) Martin & Sayles 2000, (104) JGOFS 2001 (105) Riebesell et al. 1995, (106) W. Smith, pers. comm. 1999, (107) DiTullio et al. 2000; (108) G. DiTullio, pers. comm. 2001 (109) Dayton 1990

0.5–3 cm

185

186

1985–1991, 1996, no observations austral summer 1986, 1987, 1990 1995 or older, 1996–1998, no observations austral summer 1997

1995–1998, no observations 1996

Indian Ocean W Arabian Sea

Southern Ocean Continental shelf and slope, E Weddell Sea Ross Sea

1991–1994, observatory: 1995–2001

1989–1998, continuing in June 2001, no observations summer 1997

1979–1986, no observations summer 1985 1988–2000, no observations summer 1988, 1990, 1992, 1995, 1996 1983–1987, no observations summer 1984, 1985 1985–1998, no observations summer 1987, 1991, 1993, 1994, 1995, 1997 1982 or older, 1995–1998

Overall time period of benthic sampling

Sagami Bay, Japan, NW Paciﬁc

Paciﬁc Ocean Station M, 220 km W of California, NE Paciﬁc

Bay of Biscay, NE Atlantic BIOTRANS, midoceanic abyssal plain, NE Atlantic Rockall Trough, NE Atlantic

Atlantic Ocean Porcupine Seabight, NE Atlantic Porcupine Abyssal Plain (PAP), NE Atlantic

General location

Upper ocean processes similar to PAP site Spring bloom May

Maximum in Apr.–June, episodic peak(s) in autumn n.d.

Apr.–Sept. May–Sept.

Thickest coverage in Feb.–Mar. Nov.–Dec.

Feb.–Mar. (during NE monsoon), not yet sampled during SW monsoon

(106) “modest” ﬂux in spring and early austral summer

n.d.

SW monsoon yields maximum in Aug., NE monsoon yields secondary peak in Mar.

Austral summer bloom Jan.–Mar. Early austral summer Phaeocystis bloom Nov.–Dec.

(91) Diatom bloom in June due to upwelling induced by SW monsoon; (89) primary production also increases mid-winter during NE monsoon

Spring diatom bloom Feb.–Apr.

(70) “spring plumes of chlorophyll which persist into summer”

Spring bloom Mar.–Apr.

Maximum in May

Summer

Maximum in early summer, secondary peak in autumn; (73) particulate ﬂuxes lag Bakun upwelling index by 50 days (88) maximum chl a in Apr.–May

Spring bloom May

Maximum in mid-summer

Late May–early Oct.

July–Oct., declining through Dec.; (71) “delayed ∼1.5 months after initial peaks in particulate ﬂux” Mar.–May; (87) deposition starts ∼2 wk after start of spring bloom

Spring bloom Apr.–May

General pattern for primary production in surface water

n.d.

General pattern for vertical mass ﬂux to deep water

Apr.–Sept.

General pattern for phytodetritus on sea ﬂoor

Table 2 Sites >500 m with seasonal accumulation of phytodetritus on the sea ﬂoor (plotted as closed symbols on Fig. 1). Exact timing and magnitude vary interannually. Numbers in parentheses correspond to references in Table 1. “n.d.” indicates not determined.

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ACCUMULATION OF PHYTODETRITUS ON THE SEA FLOOR

lead to ﬂuff layers, with the best example an unusual event in the New York Bight (Mahoney & Steimle 1979). Also, the massive ﬂuff layer sampled in the deep northeast Atlantic by de Wilde et al. (1998) was not linked to the spring bloom. Observations of phytoplankton blooms preceding the accumulation of phytodetritus on the sea ﬂoor are listed in Table 1. Fluff layers were observed following blooms with phytoplankton biomass (as chlorophyll a) ranging from 2.7 µg chl a l−1 in the Mediterranean (Riaux-Gobin et al. 1995) to 29 µg chl a l−1 in the equatorial Paciﬁc (Yoder et al. 1994). Initial studies in shallow waters linked phytodetritus on the sea ﬂoor to the mass sinking of phytoplankton through the water column (e.g. Graf et al. 1982, Peinert et al. 1982). The vertical, or sinking, ﬂux of particulate matter generally is measured with sediment traps. Estimates of vertical ﬂux are known to be biased when sediment traps are deployed in swift currents or near the benthic boundary layer. In deep waters during high ﬂux events, trapping efﬁciency may also be compromised due to “clogging” (e.g. Baldwin et al. 1998, Honjo et al. 1999, Honjo et al. 2000). Nevertheless, short-lived peaks (pulses) in time series of vertical ﬂux are often linked to the accumulation of phytodetritus on the sea ﬂoor. Vertical mass ﬂuxes associated with phytodetritus on the deep-sea ﬂoor, listed in Table 1, ranged from 150 mg m−2 d−1 in the Bay of Biscay (Vangriesheim & Khripounoff 1990) to 2.5 g m−2 d−1 in Sagami Bay (Kitazato et al. 2000). Particulate organic carbon (POC) ﬂuxes associated with phytodetritus on the deep-sea ﬂoor ranged from ∼10 mg C m−2 d−1 during the northeast Monsoon in the Arabian Sea (Rixen et al. 2000) to 30 mg C m−2 d−1 near the BIOTRANS site in the abyssal northeast Atlantic (Newton et al. 1994). Sometimes, even though vertical ﬂuxes were high, a ﬂuff layer did not develop on the sea bed, for example in 1993 at Station M at 4100 m in the northeast Paciﬁc (POC ﬂux peaked at 26 mg C m−2 d−1; Baldwin et al. 1998). In these cases the lack of accumulation was likely due to benthic processes such as foraging by benthic fauna, as suggested for recent years at the Porcupine Abyssal Plain site in the northeast Atlantic (Bett et al. 2001). Sometimes, the accumulation of phytodetritus on the sea ﬂoor was not associated directly with pulsed sedimentation, for example at the abyssal equatorial Paciﬁc sites (C. Smith et al. 1996) and in the Ross Sea (W. Smith, pers. comm.). Table 1 is just the beginning of a list that will probably become longer with more (and appropriate) sampling of the sea ﬂoor. Many studies that reported pulses in vertical mass and POC ﬂuxes did not have concurrent benthic sampling. Examples of such sites in coastal areas included Bedford Basin in Nova Scotia (Hargrave & Taguchi 1978). Examples in deep waters near continental margins included the Bransﬁeld Strait near the Antarctic Peninsula, “. . . where an enormous ﬂux of biogenic particles, as large as about 1 g m−2 d−1, was observed for a few weeks during the maximum austral summer” (Honjo 1996), and the Sea of Okhotsk near the Kamchatka Peninsula, where a massive ﬂux of biogenic silica occurred in spring 1991 (Honjo 1996). Examples in “high nutrient-low chlorophyll” (HNLC) areas of the open ocean included Station Papa in the northeast Paciﬁc, where a massive pulse occurred in August 1983 (POC ﬂux peaked at 55 mg C m−2 d−1; Wong et al. 1999). Examples of pulses of phytodetritus to the deep sea occurred even at oligotrophic sites, such as the Bermuda Atlantic Time Series site, which had a major event in January 1996 (Conte et al. 1998), and Station Aloha north of Hawaii, where the ﬂux of cytoplasm-containing diatoms increased 500-fold in late July 1992 and 1250-fold in August 1994 (107 cells m−2 d−1; Scharek et al. 1999). Many studies that did include benthic sampling may have missed a ﬂuff layer due to the timing of sampling or to the sampling method. For example, Pfannkuche et al. (2000) used appropriate sampling gear (i.e. multiple corer, described below) but did not sample the western Arabian Sea site (WAST) during the southwest monsoon. Studies using box corers 187

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or grabs, such as in the Northeast Water Polynya off Greenland (Ambrose & Renaud 1997) and at the southwest African continental margin (Bremner 1983), may have blown ﬂocculent phytodetritus from the sediment surface. Neaverson (1934), who described grab samples from sites near the South Georgia and South Shetland Islands, often found intact diatoms in loose, ﬂocculent aggregates.

Sampling methods At shallow depths divers may observe and carefully collect a ﬂuff layer or patch of phytodetritus (e.g. Davies 1975, Witt et al. 2001). Similarly, phytodetritus on the deep-sea ﬂoor can be observed and collected from a submersible (e.g. Lee et al. 1979, K. Smith et al. 1998). However, remote instruments are required to collect more samples over a wider geographic area and to conduct time-series investigations of phytodetritus on the sediment surface. The inﬂuential paper by Billett et al. (1983) that described the ﬂuff layer in the Porcupine Seabight combined data from time-lapse photography and a new coring device, the multiple corer, which is described below. More recent developments in technology have allowed the sampling of phytodetritus from a remotely operated vehicle (ROV; S. Goffredi & D. Stakes, pers. comm.). Sampling methods are listed for all studies on Table 1. For the studies at the 51 deep-sea sites, 9 involved a submersible, 19 included in situ photographs or video, and 30 utilised a multiple corer. Below, methods for sampling phytodetritus that do not involve diving or submersibles are described.

Photographs Photographs of the sea ﬂoor are useful in studying phytodetritus only when there is sufﬁcient contrast with the background sediment (Fig. 2). For photographs of the sea ﬂoor at Station M in the northeast Paciﬁc, detrital aggregates were deﬁned as “distinguishable in photographs by color, shading, or texture” (Fig. 2C; K. Smith et al. 1994). For example, the visual effect of phytodetritus in photographs from the Porcupine Seabight was to darken the sediment surface (Fig. 2A). In the equatorial Paciﬁc, phytodetritus created a greenish veneer on white carbonate sediment (Fig. 2B; C. Smith et al. 1996). Photographs often revealed that the distribution of phytodetritus was patchy and that phytodetritus accumulated in depressions on the sea ﬂoor, for example at the Porcupine Abyssal Plain site in August 1989 (Rice et al. 1994) and at the abyssal equatorial Paciﬁc sites (C. Smith et al. 1996). Photographs of the sea ﬂoor may be taken with cameras mounted on tethered or autonomous instrument frames. Single photographs from tethered cameras include sediment proﬁle as well as areal photographs. A sediment-proﬁle camera (Rhoads 1974) slices through the sediment/water interface and takes photographs of the distinct horizon between a ﬂuff layer and underlying sediment, for example in Long Island Sound (Fig. 1 in Stolzenbach et al. 1992). Areal photographs of phytodetritus on the sea ﬂoor have been taken from cameras mounted on coring instruments (e.g. C. Smith et al. 1996) and other instruments triggered by bottom contact (e.g. “bed-hop camera”; Black 2001). Such a technique, repeated during several cruises, was used to identify a seasonal pattern for phytodetrital accumulation on the shelf and slope in the eastern Weddell Sea (Gutt et al. 1998). 188

ACCUMULATION OF PHYTODETRITUS ON THE SEA FLOOR

Figure 2 Photographs of phytodetritus on the deep-sea ﬂoor. A. NE Atlantic, 4025 m, July 1983. A mound (indicated by arrow) protrudes above the phytodetritus that nearly carpets the sea ﬂoor. Area of photograph ∼2 m2. Oblique photograph taken with Bathysnap, courtesy R. Lampitt (Southampton Oceanography Centre, UK). B. Equatorial Paciﬁc, 4400 m, November 1992. Arrow points to sea urchin (∼10 cm wide) that created the trail through the ﬂocculent phytodetritus. Oblique photograph taken with camera sled, courtesy C. Smith (University of Hawaii, USA). C. NE Paciﬁc, 4100 m, September 1994. Arrow indicates a patch (∼50 cm across) of “rad patch”-type phytodetritus. Photograph taken through observer port of submersible alvin, courtesy K. Smith Jr (Scripps Institution of Oceanography, USA). D. Antarctic, ∼600 m, March 2001. Arrow points to holothuroid (∼10 cm long) in an area devoid of phytodetritus. Image taken from video survey, courtesy C. Smith (University of Hawaii, USA).

In order to photograph a larger area, multiple photographs (and now real-time video) from tethered cameras are usually taken as transects of the sea ﬂoor. Early studies of phytodetritus in the northeast Atlantic involved transects with a camera mounted on an epibenthic sledge (Billett et al. 1983, Thiel et al. 1988/89). Camera-sled transects also surveyed phytodetritus on the sea ﬂoor in the northwest Atlantic (21-km transects; Hecker 1990), NE Paciﬁc (∼2-km transects; Lauerman & Kaufmann 1998), and central equatorial Paciﬁc (C. Smith et al. 1996). Such transects can be analysed to quantify the distribution of phytodetritus on the sea ﬂoor, as detailed by Lauerman et al. (1996). At Station M at 4100-m depth in the northeast Paciﬁc, Lauerman & Kaufmann (1998) reported that the distribution of detrital aggregates “. . . showed signiﬁcant patchiness on scales of 1–128 m during all time periods when they were present in numbers sufﬁcient to permit examination of dispersion.” Recently, video transects have been used to survey phytodetritus on the shelf of the Antarctic Peninsula (C. Smith, pers. comm.) and in the Rockall Trough in 189

STACE E. BEAULIEU

the northeast Atlantic (Bett 2001). Video transects with an ROV enabled selective sampling of phytodetritus at 2800 m in the northeast Paciﬁc (S. Goffredi & D. Stakes, pers. comm.). In the future, transects may be conducted by autonomous vehicles such as Rover (K. Smith et al. 1997). Now, autonomous instruments are now used for time-series photographs at one location. These observations have been used to quantify the time during which phytodetritus accumulates and decomposes on the sea ﬂoor. Autonomous, or free-fall, landers include Bathysnap (Lampitt & Burnham 1983), which was the ﬁrst to photograph the rapid accumulation of phytodetritus on the sea ﬂoor in the Porcupine Seabight (Lampitt 1985) and Porcupine Abyssal Plain (Bett et al. 2001). Another camera tripod, which photographs a larger area (20 vs. 2 m2; K. Smith et al. 1993), has been deployed over an approximately 10-yr period at Station M in the northeast Paciﬁc. Other autonomous tripods or platforms yielding photographs of phytodetritus on the sea ﬂoor included STABLE in the North Sea (Williams et al. 1998), BOM in the abyssal equatorial Paciﬁc (Gardner et al. 1984), and the BOLAS lander at the Porcupine Abyssal Plain site (Witbaard et al. 2000). The longest continuous time series of photographs at a single location in the deep sea is at a sea ﬂoor observatory in Sagami Bay off Japan (Kitazato et al. 2000). Real-time video from the observatory has shown the deposition of phytodetritus at 1177 m every spring since 1994.

Cores Collecting samples at shallow depths with tube corers, Petersen (1918) gave an early description of a sort of ﬂuff layer on top of more consolidated sediment: “. . . the glass tube samples showed that above this [black malodorous mass of sulphurous mud] lay a thin layer of different composition, brown or greyish in colour, containing, besides ﬁne, inorganic particles, also minute vegetable remains.” The development of the multiple corer (Barnett et al. 1984) in the early 1980s allowed a remote means of collecting excellent quality tube cores in the soft sediment of the deep sea. A multiple corer enables sample collection without disturbing the delicate sediment/water interface (Blomqvist 1991). New versions of the original multiple corer include the “megacorer” used recently in the Rockall Trough (Bett 2001) and near the Antarctic Peninsula (C. Smith, pers. comm.). Multiple corers have yielded samples of phytodetritus with quality similar to that of cores taken by divers in shallow water (e.g. Graf et al. 1982) or by a submersible in deep water (e.g. Sibuet et al. 1990, Beaulieu & Smith 1998). With new video technology for targeting the landing, a multiple corer can yield samples as good as those selected for coring by the pilot of a submersible. In sandier or variable bottoms a “Haps” corer with a single tube has been successful in collecting phytodetritus (e.g. Kanneworff & Christensen 1986, G. DiTullio, pers. comm.). Photographs of phytodetritus sampled in tube cores are shown in Figure 3. Other corers such as box corers have had limited success in collecting phytodetritus due to the “bow wave effect” that blows ﬂocculent material from the sediment surface. Two deep-sea studies that used both box and multiple corers indicated the superiority of the multiple corer in sampling phytodetritus (Thiel et al. 1988/89, C. Smith et al. 1996). A box corer has sampled adequately on occasions when the ﬂuff layer was gelatinous rather than ﬂocculent (e.g. Pilskaln 1991, de Wilde et al. 1998). Box corers have also collected phytodetritus in lumens of burrows (Aller & Aller 1986, C. Smith et al. 1996). Other devices such as grabs and dredges are not generally capable of sampling phytodetritus.

190

ACCUM ULATI ON OF PHYTODETRI T U S O N T H E S E A F L O O R

Figure 3 Photographs of phytodetritus sampled in tube cores. A. NE Atlantic, looking down onto sample taken by multiple corer, 2000 m, June 1985. Pen points to discrete, gelatinous aggregate amidst ﬂocculent phytodetritus. Core diameter 6 cm. Courtesy R. Lampitt (Southampton Oceanography Centre, UK). B. NE Atlantic, multiple corer sample from BENBO Site C, 1920 m, August 1997. Core diameter 6 cm. Courtesy K. Black (Dunstaffnage Marine Laboratory, UK). C. NE Paciﬁc, alvin tube core sample, 4100 m, August 1994. Core diameter 7 cm. Photograph by S. Beaulieu. D. Southern Ocean, multiple corer sample from ANTARES I cruise, Station KTB05, 3615 m, April 1993. Core diameter 6 cm. Photograph by D. Moriarty, courtesy C. Riaux-Gobin (Laboratoire Arago, France).

Cores allow measurement of the thickness of ﬂuff layers (Table 1). The thickest ﬂuff layers were sampled from a trough in the Antarctic shelf (25 cm; Barthel 1997) and from a submarine canyon in the Porcupine Seabight (30 cm; Tudhope & Scofﬁn 1995). The thickness of most ﬂuff layers, from the shallow and deep sea, tends to be on the order of 1 cm.

191

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Cores also allow harvesting of phytodetritus separately from the underlying sediment. Generally, the top water is siphoned from the core and then phytodetritus is “slurped” from the sediment surface with a pipette (Lochte & Turley 1988, C. Smith et al. 1996, K. Smith et al. 1998, Kitazato et al. 2000), syringe (de Wilde et al. 1998), or by continued siphoning (Riaux-Gobin et al. 1997). Discrete gelatinous aggregates can be collected with soft forceps (Riemann 1989).

Composition of phytodetritus The macroscopic appearance of phytodetritus ranged from ﬂocculent/ﬂuffy to gelatinous/ mucousy, with colour described as green, brown, or grey (Table 1). Some authors suggested that appearance was related to the age, or state of degradation, of phytodetritus. For example, Gooday & Turley (1990) suggested a colour transition with age from green to white for phytodetritus in the northeast Atlantic. Other generalisations for phytodetritus in the northeast Atlantic included a change in appearance from ﬂocculent, ﬂuffy, green “carpets” due to the spring bloom to gelatinous, sticky, grey–green aggregates due to blooms in summer and autumn (de Wilde et al. 1998). One might assume that macroscopic appearance would be correlated to the microscopic and chemical composition of phytodetritus. However, as detailed below, very few data exist to provide a convincing argument for (or against) a direct relationship between appearance and composition.

Microscopic composition Although describing phytodetritus from the northeast Atlantic, the following statement by Gooday & Turley (1990) holds for phytodetritus collected from the deep-sea ﬂoor in a wide range of locations: Phytodetritus contains a wide variety of planktonic remains, including those of diatoms, coccolithophorids, dinoﬂagellates, silicoﬂagellates, phaeodarians, tintinnids, foraminifers, crustacean eggs and moults, protozoan faecal pellets (“minipellets”), nano- and pico-plankton cells, embedded in a gelatinous and membranous matrix. Scanning electron micrographs (SEM) of samples from various ocean basins show the variety of components in deep-sea phytodetritus (Fig. 4). This variety is expected based on the microscopic composition of marine snow, reviewed by Fowler & Knauer (1986) and Alldredge & Silver (1988). Most of the listings of microscopic components on Table 1 included a combination of several types of phytoplankton. The following four types, outlined below, also dominated the composition of phytodetritus: diatoms, coccolithophorids, Phaeocystis spp., and dinoﬂagellates. The presence of intact phytoplankton cells in phytodetritus on the deep-sea ﬂoor is often considered to imply rapid sedimentation. For example, in describing phytodetritus from the abyssal BIOTRANS site in the northeast Atlantic, Gooday & Turley (1990) stated, “The presence of relatively undegraded algal cells and viable cyanobacteria reﬂect its rapid sedimentation.” Studies of phytodetritus collected at other deep-sea sites also detected the 192

ACCUM ULATI ON OF PHYTODETRI T U S O N T H E S E A F L O O R

Figure 4 SEM images showing microscopic composition of deep-sea phytodetritus. A. NE Atlantic (ﬂuff as in Fig. 3B). Mainly coccoliths. Scale bar 10 µm. Courtesy K. Black (Dunstaffnage Marine Laboratory, UK). B. Mediterranean, sample from MEDIMAR-2 cruise, Station R5, 1584 m, March 1991. Diatom chain (Skeletonema costatum) diagonal across centre. Scale bar 25 µm. Courtesy C. Riaux-Gobin (Laboratoire Arago, France). C. NE Paciﬁc (ﬂuff as in Fig. 3C). Diatom (Asteromphalus sp.) in lower left, polycystine radiolarian in upper right. Scale bar 50 µm. Photograph by S. Beaulieu. D. Southern Ocean (ﬂuff in Fig. 3D). Diatom chain (Fragilariopsis kerguelensis) ﬁlls right half of image. Scale bar 20 µm. Courtesy C. Riaux-Gobin (Laboratoire Arago, France).

autoﬂuorescence of chlorophyll a in intact phytoplankton cells (C. Smith et al. 1996, RiauxGobin et al. 1997, Beaulieu & Smith 1998). However, we must be cautious in interpreting rapid sedimentation, especially when considering that some phytoplankton cells may remain viable for long periods in cold, aphotic conditions (e.g. Kimball et al. 1963, Smetacek 1985).

Diatoms Diatoms, known for seasonal blooms such as the spring bloom in temperate waters, are responsible for many of the ﬂuff layers in Table 1, including most of the locations with seasonal deposition of phytodetritus (Table 2; closed symbols on Fig. 1). Diatom blooms are frequently terminated by aggregation and mass sinking (e.g. Smetacek 1985, Alldredge et al. 1995). Diatoms are distinguished by their siliceous frustules, which in many species are connected together in chains. Diatoms are nonmotile but some species may control their buoyancy and regulate vertical position in the water column (Sancetta et al. 1991). Some 193

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species also form heavily siliciﬁed resting spores as a response to unfavourable conditions (Tomas 1997). Often, ﬂocculent /ﬂuffy phytodetritus is composed of a combination of species of chain-forming planktonic diatoms such as Chaetoceros spp. and Rhizosolenia spp. (Table 1). Sometimes, the composition of phytodetritus on the sea ﬂoor is dominated exclusively by one species, for example Chaetoceros socialis in Loch Ewe (Davies 1975), Detonula confervacea in the Baltic Sea (Graf et al. 1982), Paralia (Melosira) sulcata in the North Sea (C. Grenz, pers. comm.), Corethron criophilum in the Antarctic (Barthel 1997), and Thalassiosira hyalina in the northwest Atlantic (Aller & Aller 1986). Older reports of viable diatom cells in the deep sea include revival tests in which culture media were inoculated with sediment or water samples (e.g. Kimball et al. 1963, Malone et al. 1973). Recent revival tests for phytodetritus sampled at depths to 4700 m in the Southern Ocean yielded diatom cultures dominated by Chaetoceros spp., known for resting spores (Riaux-Gobin et al. 1997).

Coccolithophorids Coccolithophorids are the second most common type of phytoplankton forming the bulk of phytodetritus (Table 1). These single-celled algae are distinguished by protective calcareous plates, or coccoliths, composed of calcium carbonate. Large blooms of coccolithophorids, often in temperate and arctic waters, are recognised in satellite images (e.g. Conte et al. 1995). The most common species, Emiliania huxleyi, is nonmotile during its coccolithbearing phase (Tomas 1997) and may form mucous aggregates such as the “macroaggregates” collected in sediment traps following a bloom in the North Sea (Cadee 1985). Other species also form rapidly-sinking aggregates, such as Umbellicosphaera sibogae, which was collected in sediment traps moored deep in the Panama Basin (Honjo 1982). Coccolithophorids were ﬁrst reported in phytodetritus sampled at the abyssal BIOTRANS site in the northeast Atlantic (Lochte & Turley 1988). Coccolithophorids are common components of phytodetritus at other locations in the deep northeast Atlantic, including the Porcupine Seabight and Rockall Trough (Scottish Marine Biological Association 1982). Speciﬁcally, Emiliania huxleyi was identiﬁed as a dominant component in phytodetritus from the continental slope south of the Porcupine Seabight (de Wilde et al. 1998) and the Iceland Basin in the subarctic north Atlantic (Conte et al. 1995).

Other phytoplankton Other phytoplankton that dominated the composition of phytodetritus included Phaeocystis spp. and, rarely, dinoﬂagellates. Algae in the genus Phaeocystis are related to coccolithophorids (in Class Prymnesiophyceae) but do not have calcareous scales (Tomas 1997). Phaeocystis spp. have a solitary, motile stage as well as a nonmotile stage in which cells are embedded in gelatinous colonies (Tomas 1997). Two weeks after a major bloom in the North Sea, ﬂuff composed of Phaeocystis sp. was observed to accumulate between sand ripples during slack tide (Jago & Jones 1998). Phaeocystis sp. was also observed in “dense accumulations” on the shallow bed in McMurdo Sound in March 1977 (Dayton 1990). Observations of ﬂuff layers composed of Phaeocystis spp. in the deep sea are rare (although perhaps seasonal in the Ross Sea; G. DiTullio, pers. comm.), probably because organic aggregates composed of decaying Phaeocystis colonies are recycled in the water column (Wassmann et al. 1990, Passow & Wassmann 1994). 194

ACCUM ULATI ON OF PHYTODETRI T U S O N T H E S E A F L O O R

Dinoﬂagellates, motile phytoplankton with complex life cycles and a variety of morphologies, may bloom in episodic and spectacular “red tides” (Tomas 1997). Although mass sinking of dinoﬂagellate cysts has been observed, for example following a bloom in the Baltic Sea (Heiskanen 1993), dinoﬂagellates usually do not form marine snow aggregates (with some exceptions; e.g. Alldredge et al. 1998). Although often noted as a component of phytodetritus, only a single ﬂuff layer on Table 1 was caused exclusively by dinoﬂagellates, speciﬁcally by a red tide of Ceratium tripos in the New York Bight (Mahoney & Steimle 1979). Cyanobacteria, or blue–green algae, are responsible for ﬂuff layers in lakes (e.g. Otten et al. 1992, Goedkoop & Johnson 1996) but not in the ocean. However, small coccoid cyanobacteria are autoﬂuorescent and notably abundant in deep-sea phytodetritus (Lochte & Turley 1988, Beaulieu & Smith 1998). In addition to phytoplankton, several other microscopic components are associated with the matrix of phytodetritus. For example, radiolarians (speciﬁcally phaeodarians) are abundant in phytodetritus from the abyssal northeast Atlantic (Riemann 1989) and northeast Paciﬁc (Beaulieu & Smith 1998). Phaeodarian faecal pellets (“minipellets”) can be produced within or entrained by phytodetritus sinking through the deep water column (Riemann 1989). Siliceous radiolarian skeletons may persist along with diatom frustrules as “benthic ﬂoc” long after the organic matter in phytodetritus has been remineralised (e.g. Reimers & Wakeﬁeld 1989).

Chemical composition In general, the chemical composition of the ﬂuff layer differs (sometimes radically) from the underlying sediment. The phytopigments, organic carbon, and inorganic contents of phytodetritus sampled from the sea ﬂoor are summarised below (Table 3). Most of the studies cited below had very limited sample sizes (i.e. few replicate measurements). Although many studies attributed high values of phytopigments or organic carbon in surﬁcial sediment to phytodetritus (e.g. Pfannkuche 1993), the discussion below is restricted to only those studies that harvested phytodetritus separately from the underlying sediment. All values are reported as per unit dry mass.

Phytopigments As expected, values for phytopigment (i.e. algal pigment) concentration in phytodetritus are higher than in underlying sediment (Table 3). Older, ﬂuorometric methods for determining phytopigment concentrations generally extract chlorophyll a (chl a) and phaeopigments as bulk values. Phaeopigments are a combination of degradation products of chlorophyll, usually dominated by phaeophorbides (particularly, phaeo a). Newer, spectrophotometric methods allow for quantiﬁcation of compounds speciﬁc to particular phytoplankton groups in addition to the determination of bulk chl a and phaeopigment content. Some deep-sea studies reported only the total chloropigments, or chloroplastic pigments, which ranged from 1.6 µg g−1 in phytodetritus from the abyssal equatorial Paciﬁc (C. Smith et al. 1996) to 78 µg g−1 in the Porcupine Seabight (Rice et al. 1986). However, taking into account values reported for chl a and phaeopigments, the range for total chloropigments in deep-sea phytodetritus was broader than reported in shallow-water studies. The overall range for chl a, 0–129 µg g−1, was also the range in a single study at Station M at 4100 m in the NE 195

196

July–Aug. 1986

July 1998

BIOTRANS, midoceanic abyssal plain, NE Atlantic

Rockall Trough, NE Atlantic

n.d.

n.d.

n.d.

(20) 206– 407 ng cm−3, 23–26% of total chloropigments

n.d. Decreased from 78 µg g−1 at 1008 m to 2.4 µg g−1 at 4535 m 2.2–13.1 µg g−1 n.d.

(4) May 1984

Aug. 1995

Decreased by 2 5.4–17.8% of total orders of chloropigments magnitude from 1400 to 4500 m

Chl a Total chloropigments

(2) July 1982

Date sampled

NW European continental slope, NE Atlantic

Atlantic Ocean Porcupine Seabight, NE Atlantic

General location

n.d.

(11) Phaeophorbides 40–60% of total chloropigments; (12) signiﬁcant differences in pigment composition among stations (20) Phaeo a/chl a ratio 1.6–2.0

Phaeo a most abundant, no chl b, some chl c and fucoxanthin-type carotenoids, similar pigment composition at all depths Phaeo a/chl a ratio increased from 5.2 at 1008 m to 17.3 at 4535 m

Other phytopigments

n.d.

(19) 8.8– 78.5 mg g−1; (20) 0.9–7.9% (mean 2.3%), 70–390 mg m−2; (23) ∼15 mmol m−2 (30) 0.45%; (33) 775 µmol g−1, 0.9%

n.d.

n.d.

n.d.

n.d.

δ13Corg

(11) 3 g m−2; (12) 250 mmol m−2

2030 m: 0.56%, 4535 m: 1.28%

5.98%

Organic carbon (Corg)

(30) 6; (33) 7

(19) 7

Increased from 9 at 1008 m to 24 at 4535 m n.d.

24

C : N ratio

(30) Amino acids 2 mg g−1, aldoses 0.5 mg g−1

(20) Carbohydrates 6–28 mg C m−2, proteins 85– 217 mg C m−2

(33) CaCO3 52–56%

n.d.

CaCO3: 1336 m: 34%, 4535 m: 62%b n.d.

% PUFAa of total fatty acids: 2030 m: 12%, 4535 m: 6%

(11) Rich in 4MeC27:0 sterol; (12) DNA 49– 202 µg g−1, RNA 20–245 µg g−1, RNA/ DNA ratio up to 1.2

n.d.

n.d.

Other organic content Inorganic content

n.d.

n.d.

n.d.

n.d.

n.d.

Radionuclide activity

Table 3 Chemical composition of phytodetritus (analysed separately from underlying sediment). Values are per unit (or %) dry weight. Single values represent means unless otherwise indicated. Chloropigments generally represent the sum of chlorophyll a (chl a) and phaeopigments. C : N ratio by weight (rounded to whole number). Numbers in parentheses correspond to references in Table 1. “n.d.” indicates not determined. Update of table 4 in K. Smith et al. 1998.

STACE E. BEAULIEU

197

Paciﬁc Ocean Sta. M, 220 m W of California, NE Paciﬁc (76) 0–129 (mean 25) µg g−1 (76) 0–28 (mean 3) µg g−1

n.d.

n.d.

Sept. 1994

n.d.

n.d.

Aug. 1994

n.d.

n.d.

n.d.

n.d.

(51) I. Station: Aug. 1976

Mediterranean Sea Gulf of Lions, W (60) Apr. 1991 Mediterranean Sea Black Sea Apr.–May 1988

n.d.

n.d.

(51) DOS 1: July 1976

Continental slope, NW Atlantic

n.d.

(47) July 1991b

Iceland Basin, NE Atlantic

(39) Up to 30 µg g−1; (40) up to 90 µg g−1 n.d.

n.d.

(41) Mar.–Apr. n.d. 1980; (44) Dec. 1983; (45) Oct. 1996– Dec. 1998

Baltic Sea

n.d.

(39) May 1985; (40) Apr. 1983

Øresund, Denmark

(76) Phaeopigments 0.3–3.7 (mean 1.5) mg g−1 (76) Phaeopigments 0.1–3.3 (mean 1.4) mg g−1

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

(39) Phaeopigments up to 180 µg g−1

(76) 24–99 (mean 51) mg g−1

(76) 5–110 (mean 46) mg g−1

(64) 3.2%; (65) 7%; (66) 7–8%

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

(41) Increased from 6 in Mar. to 10 in Apr.; (45) 10 n.d.

n.d.

n.d.

n.d.

(76) 8

(76) 7

(64) 12 (64) −24.5‰

−31‰

n.d.

n.d.

n.d.

(45) −24‰

(41) 10 g m−2; (44) 8.9 g m−2; (45) 4.37%

n.d.

n.d.

n.d.

(76) ATP 0–283 (mean 19) ng g−1

(76) ATP 1–275 (mean 19) ng g−1

(64) Total sterols 325 µg g−1, individual sterols in their Fig. 1c, total fatty acids 137 µg g−1, individual fatty acids in their Fig. 2c; (65) combustible organic matter 15%

n.d.

Total sterols up to 25 µg g−1, LCK + AAc up to 12 µg g−1, PUFAa up to 6.5 µg g−1 SEFd sterols 1.5 µg g−1, NSESd sterols 2 µg g−1 SEFd sterols 0.7 µg g−1, NSESd sterols 1.1 µg g−1

n.d.

(39) Combustible organic matter up to 9%

(76) CaCO3 2–21% (mean 7%)

(76) CaCO3 3–35% (mean 12%)

CaCO3: (65) 53%, (66) 31%; biogenic silica: (65) 7%, (66) 6%; lithogenic: (66) 47%

n.d.

n.d.

n.d.

n.d.

(45) Opal 10.3%

n.d.

(72) Excess 210Pb 172 dpm g−1, excess 234Th 2050 dpm g−1

n.d.

n.d.

n.d.

n.d.

Excess 210Pb ∼30 dpm g−1

n.d.

n.d.

ACCUMULATION OF PHYTODETRITUS ON THE SEA FLOOR

n.d.

(88) May 1997

198

n.d.

e

d

c

b

n.d

n.d.

n.d.

0.6–0.7%

n.d.

(92) Phaeo a up to n.d. 157 µg l−1, lowest phaeo a/chl a ratio 12.5, phaeo c up to 70 µg l−1

Phaeopigments 1150 ng cm−3, fucoxanthin 108 ng cm−3, peridinin 7–8 ng cm−3

0.16%

1–2%, 2.6 mmol m−2

Organic carbon (Corg)

continued

polyunsaturated fatty acids. some mixture with underlying sediment. long-chain alkenones and alkyl alkenoates. Soxhlet-extractable free and non-Soxhlet-extractable saponiﬁed sterols. PL phospholipids, HC hydrocarbons, FFA free fatty acids, MAG monoacylglycerols, TAG triacylglycerols.

n.d.

n.d.

(95) 1989 and Apr. 1991 (104) Mar. 1998

Atlantic sector of Southern Ocean Paciﬁc sector of Southern Ocean

a

n.d.

n.d.

Apr. 1993

Southern Ocean Indian sector of Southern Ocean (92) Up to 13 µg l−1, chl a/chl b ratio up to 6, chl a/chl c ratio up to 7.8

120 ng cm−3

n.d.

n.d.

Mar. 1987

Magellan Rise seamount, W equatorial Paciﬁc Sagami Bay, Japan, NW Paciﬁc

n.d.

Up to 0.06 µg g−1, up to 5% of total chloropigments n.d.

Up to 1.6 µg g−1

(83) Nov.–Dec. 1992b

Midoceanic abyssal plain, central equatorial Paciﬁc

Other phytopigments

Total Chl a chloropigments

Date sampled

General location

Table 3

(93) Total lipids 1033 µg l−1, with contribution from the following lipid classes: PL 37%, HC 36%, FFA 13%, MAG 10%, and TAG 5%e n.d. n.d.

n.d.

n.d. n.d.

n.d.

−24 to −26.5‰ n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

Biogenic silica 4%

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

7–9

n.d.

n.d.

Inorganic content

Other organic content

C : N ratio

δ13Corg

Excess 210Pb 44–81 dpm g−1, excess 234Th 74–216 dpm g−1

n.d.

n.d.

n.d.

n.d.

Excess 210Pb 70 dpm g−1, excess 234Th 25 dpm g−1

Radionuclide activity

STACE E. BEAULIEU

ACCUMULATION OF PHYTODETRITUS ON THE SEA FLOOR

Paciﬁc, indicating signiﬁcant heterogeneity among samples of phytodetritus taken at the same time (n = 55 samples in August 1994; K. Smith et al. 1998). Phaeopigments ranged from a very small quantity, 157 µg l−1 in ﬂuff from the Southern Ocean (Riaux-Gobin et al. 1997), to 3700 µg g−1 in the above samples from Station M (K. Smith et al. 1998). In the Porcupine Seabight, phytopigment content in phytodetritus tended to decrease with depth (Billett et al. 1983, Rice et al. 1986) but there are not enough data to show this decrease as a general phenomenon at other continental margins. Although not an absolute indication of age, the percentage contribution of chl a to the total chloropigments indicates the relative “freshness” or “quality” of phytodetritus (e.g. Duineveld et al. 2000). Values of chl a as a percentage of total chloropigments ranged from 5% in the Porcupine Seabight (Billett et al. 1983) to 26% at the BIOTRANS site (Thiel et al. 1988/89). Speciﬁcally, the phaeo a to chl a ratio has been used to indicate the quality of deep-sea phytodetritus, ranging from 1.6, indicating fresh phytodetritus at the BIOTRANS site (Thiel et al. 1988/89), to 17.3, indicating refractory phytodetritus in the Porcupine Seabight (Rice et al. 1986). Arguably, chl a and phaeopigments can be used as tracers of labile organic carbon in phytodetritus (Stephens et al. 1997, Boon & Duineveld 1998). The half-life for degradation of chl a ranged from 9–38 days at shallow depths in Long Island Sound (depending on temperature; table 3 in Sun et al. 1994) and from 3–87 days in the abyssal equatorial Paciﬁc (table 2 in Stephens et al. 1997). However, some chl a may be “protected” from degradation in intact cells such as resting spores (Stephens et al. 1997). Several studies used high pressure liquid chromatography (HPLC) to determine the concentration of individual phytopigments, some of which are speciﬁc to algal taxa. HPLC chromatograms can be used as “ﬁngerprints” to compare samples of phytodetritus from different sites and determine whether the samples derived from the same bloom, as discovered in the Porcupine Seabight (Billett et al. 1983), or different blooms, as discovered along the slope to the south of the Porcupine Seabight (de Wilde et al. 1998). HPLC chromatograms were also used to trace a coccolithophorid bloom from surface waters to the deep-sea nepheloid layer (Conte et al. 1995) and ﬂuff layer (M. Conte, pers. comm.) in the Iceland Basin.

Organic carbon Depending on the study, very different conclusions have been drawn about the relative importance of phytodetritus in supplying organic carbon to the sea ﬂoor. For example, C. Smith (1994) wrote, “Biochemical analyses suggest that organic carbon, protein, and lipid contents of fresh phytodetrital material can be substantially higher than typical deep-sea surﬁcial sediments, and that much of the organic content is labile.” However, Rice et al. (1994) stated, “. . . available data suggest that the organic matter content of phytodetritus arriving at the sea bed is both variable and rather low.” A recent study in the Rockall Trough found organic carbon in the ﬂuff layer comparable with, and sometimes lower than, underlying sediment (Black 2001). Overall, the organic carbon content of phytodetritus from the deepsea ﬂoor ranged widely (Table 3) from 0.45% in the Rockall Trough to a maximum of 11% at Station M in the northeast Paciﬁc (K. Smith et al. 1998), with only 0.16% in “benthic ﬂoc” from the Magellan Rise (Reimers & Wakeﬁeld 1989). Several studies considered the spatial distribution and thickness of phytodetritus on the sea ﬂoor and reported the total load of organic carbon per unit area. Loadings for organic carbon from phytodetritus on the deepsea ﬂoor ranged from 2.6 mmol C m−2 in the abyssal equatorial Paciﬁc in November 1992 199

STACE E. BEAU L I E U

(C. Smith et al. 1996) to 250 mmol C m−2 at the continental slope in the northeast Atlantic in August 1995 (de Wilde et al. 1998). These loadings can be compared with annual sediment trap ﬂuxes to determine the relative magnitude of the deposition event. For example, Lampitt & Antia (1997) reported that POC ﬂuxes at 2000 m tend to be about 2 g C m−2 y−1 at the Porcupine Abyssal Plain site, offshore of the stations sampled by de Wilde et al. (1998). The ﬂuff layer sampled by de Wilde et al. (1998) in August 1995, with its loading of 3 g C m−2, likely contributed most of the ﬂux to the benthic community for that entire year. Loadings can also be compared with estimates of primary production in surface waters to determine the percentage of production exported to the sea ﬂoor. For example, in the Baltic Sea, Graf et al. (1982) found an organic carbon load of 10 g (or 833 mmol) C m−2 in the sediment, corresponding to ∼50% of the production during the spring diatom bloom. The carbon load of the ﬂuff layer in July 1986 at the abyssal BIOTRANS site in the northeast Atlantic was estimated to be up to 3% of the spring primary production (Thiel et al. 1988/89). The organic carbon content of phytodetritus gives no information about the source or lability of the carbon. Stable isotopes such as 13C and 15N can be used to trace the source of organic matter (e.g. Peterson et al. 1985). For example, characteristic δ13Corg values for marine phytoplankton are −18‰ to −21‰ (Bruland et al. 1989). The δ notation indicates the depletion (−) of the heavy isotope relative to the lighter isotope (Peterson et al. 1985). Values for δ13Corg in phytodetritus ranged from −24‰ in the Baltic Sea (Löfﬂer et al. 2000) and Southern Ocean (Mackensen et al. 1993) to −31‰ in the Mediterranean Sea (RiauxGobin et al. 1995). At the Southern Ocean sites in particular, δ13Corg was 2–3‰ more depleted in the ﬂuff compared with underlying sediment (Mackensen et al. 1993). The organic carbon to nitrogen (C : N) ratio is often used to indicate the lability of organic matter, assuming that nitrogen-containing compounds, such as proteins, are more labile. The C : N ratio (by weight) for phytoplankton is generally on the order of 6 (the Redﬁeld ratio). C : N ratios (rounded to the nearest whole number) for phytodetritus sampled from the sea ﬂoor ranged from 6 in the Baltic Sea (Graf et al. 1982) and Rockall Trough (Good et al. 2000) to 24 in the Porcupine Seabight (Billett et al. 1983). Although the C : N ratio increased with depth in the Porcupine Seabight, this depth-related increase does not appear to be a general trend. Phytodetritus sampled at other sites >4000 m had low C : N ratios, for example at Station M in August 1994 (mean 7, n = 53) and in September 1994 (mean 8, n = 85; K. Smith et al. 1998) and at the abyssal equatorial Paciﬁc sites (range 7–9, n = 9; C. Smith et al. 1996). As described above for phytopigments, speciﬁc organic compounds can indicate the relative freshness or quality of phytodetritus. For example, Graf (1992) suggested, The enrichment of fresh organic matter at the sediment surface observed after spring bloom sedimentation will cause increased amino acid concentrations at the sediment surface, especially when the fragile plankton cells are disrupted and start to leach. However, the only actual measurement of amino acids in ﬂuff (2 mg g−1) was reported for the ﬂuff sampled in the Rockall Trough in July 1998, which had relatively low organic content (Good et al. 2000). Polyunsaturated fatty acids are used also as an indicator of relative freshness, and Rice et al. (1986) found a decreasing trend with depth in the Porcupine Seabight. Several studies also reported concentrations of sterols, with the highest content 325 µg g−1, in the “permanent” ﬂuff layer in the Black Sea (Beier et al. 1991). PinturierGeiss et al. (2001) reported higher concentrations of phospholipids and triacylglycerols in 200

ACCUM ULATI ON OF PHYTODETRI T U S O N T H E S E A F L O O R

ﬂuff compared with underlying sediment at a Southern Ocean site (378 µg l−1 and 47 µg l−1, respectively).

Inorganic contents Values for the inorganic composition of phytodetritus are rarely reported (Table 3). The percentage of calcium carbonate (% CaCO3) in phytodetritus, which gives some indication of the relative abundance of coccolithophorids, ranged from 2% at Station M in the northeast Paciﬁc (K. Smith et al. 1998) to 62% in the Porcupine Seabight (may have included some underlying sediment; Rice et al. 1986). The percentage of opal (% biogenic silica), which gives some indication of the relative abundance of diatoms, ranged from 4% in “benthic ﬂoc” on the Magellan Rise (Reimers & Wakeﬁeld 1989) to 10% in the Baltic Sea (Löfﬂer et al. 2000). The largest percentage of biogenic silica reported for phytodetritus from a deepsea site is 7% in ﬂuff from the Black Sea (Pilskaln 1991). Three studies also determined the activity (in disintegrations min−1) of radionuclides in phytodetritus. Radionuclides that adsorb to sinking particles, such as 210Pb and 234Th, may be used to determine the relative age of phytodetritus on the sea ﬂoor (as compared with particles collected in sediment traps). For example, 234Th, a radionuclide produced throughout the water column by 238U decay, has a relatively short half-life (24.1 days) compared with 210 Pb (22.3 yr) and can be used to track recently-settled particles such as fresh phytodetritus. The excess (unsupported by decay of parent radionuclide) activity of 210Pb ranged from 44 dpm g−1 in phytodetritus sampled at a Southern Ocean site (Joint Global Ocean Flux Study 2001) to a mean of 172 dpm g−1 at Station M in the northeast Paciﬁc (n = 3; Lauerman et al. 1997). The excess activity of 234Th ranged from 25 dpm g−1 in phytodetritus from the abyssal equatorial Paciﬁc (C. Smith et al. 1996) to a mean of 2050 dpm g−1 at Station M (n = 3; Lauerman et al. 1997). In the study by Lauerman et al. (1997), phytodetritus on the sea ﬂoor was the same age as material collected in a sediment trap 50 m above bottom and considerably younger than the underlying sediment.

Mechanisms leading to mass sinking events The accumulation of phytodetritus on the sea ﬂoor is often linked to the mass sinking of phytoplankton blooms. Rapid, pulsed sedimentation of phytoplankton through the water column has been documented in shallow and deep waters (references in Smetacek 1985, Legendre 1990 and Conte et al. 2001). Rapid sedimentation requires that cells aggregate into larger particles (marine snow) so that they have faster sinking speeds. Three factors important for the aggregation of phytoplankton are abundance and stickiness of cells and other particles and turbulent ﬂuid shear to bring these particles in contact. The relative importance of biological and physical factors in the aggregation of phytoplankton is an active area of research (e.g. Riebesell 1991a,b, Alldredge et al. 1995, Kiørboe 2001). Although a precursor to rapid sedimentation, aggregation does not always result in the mass settling of a bloom (e.g. Passow & Wassmann 1994, Kiørboe et al. 1998). Mass sinking also requires the decoupling of primary production from grazing and the microbial loop in the water column (Legendre 1990). In the open ocean the pelagic food web is tightly coupled to primary production and on average only about 1% of the primary production is exported to deep 201

STACE E. BEAU L I E U

waters (e.g. Lampitt & Antia 1997, Wong et al. 1999). However, processes in the upper water column sometimes allow more of the primary production to escape recycling, increasing the ﬂux of phytodetritus to the sea ﬂoor. As hypothesised by Bruland et al. (1989), “. . . the sedimentary record may be greatly biased towards these relatively rare uncoupled events rather than the ‘norm’ of leakage from the coupled biological system of the euphotic zone.” Although generally due to pulsed sedimentation, in certain locations a ﬂuff layer may develop from more continuous vertical ﬂux. As cautioned by Reimers & Wakeﬁeld (1989), “all occurrences of aggregated biogenic particles on the sea ﬂoor do not signal the sudden arrival of rapidly sinking, surface-derived aggregates.” Fresh phytodetritus may accumulate in locations where benthic community metabolism is low, such as in sub- or anoxic conditions, or where sediment focusing due to submarine topography supplies large quantities of phytodetritus. For example, phytodetrital accumulation was observed at sites that lie within the oxygen minimum zone in Sagami Bay (Kitazato et al. 2000) and on the Volcano 7 seamount (Wishner et al. 1990). Anoxic conditions probably contributed to the persistence of ﬂuff layers in Eckelfoerde Bay in the Baltic Sea (summer/autumn; E. Sauter, pers. comm.), Guaymas Basin, and the Black Sea (Pilskaln & Pike 2001). Sediment focusing and lack of resuspension were suggested as reasons for the accumulation of ﬂuff layers in the Pomeranian Bight in the Baltic Sea (Witt et al. 2001), the Gollum Channel in the Porcupine Seabight (Tudhope & Scofﬁn 1995) and in a trough in the Antarctic shelf (Barthel 1997). For the cases in Table 1 that probably resulted from mass sinking of phytoplankton blooms, concurrent physical processes were noted in the upper water column that may have contributed to the pulsed events by enhancing primary production, aggregation of phytoplankton, and/or sedimentation of marine snow. The mechanisms are grouped under four headings, outlined below: a) mixed layer effects, b) mesoscale eddies, c) fronts, and d) ice edge effects. Of the 61 sites where phytodetritus was observed (Fig. 1), approximately half (33) could be listed under one (or a combination) of the physical processes. Twenty of the 61 sites were not assigned due to lack of information on concurrent processes in the upper water column.

Mixed layer effects The mixed layer is the upper region of the water column, mixed by wind stirring and buoyancy ﬂuxes, separated from deeper waters by a density gradient, or pycnocline. Mixing enhances primary production by injecting nutrients into surface waters but net production cannot occur if phytoplankton are mixed below the “critical depth” (i.e. the depth at which integrated production equals the integrated respiration of phytoplankton). Mixing enhances the aggregation of phytoplankton by increasing the contact rate of particles. The mass aggregation of diatom blooms, in particular, has been correlated with wind-generated turbulence (e.g. Tiselius & Kuylenstierna 1996). Mixing also transports phytoplankton and aggregates deeper into the water column, directly coupling surface waters with the sea ﬂoor in well-mixed systems. Hargrave (1980), in a review of benthic–pelagic coupling in shallow waters, determined that phytoplankton are more likely to be deposited directly to the sea ﬂoor when the water column is well-mixed or has a deep mixed layer. Riaux-Gobin et al. (1995) suggested that deep mixing caused a ﬂuff layer at 1584 m in the Mediterranean, concluding, “Homogeneous hydrological conditions favoured the accumulation of this deep 202

ACCUMULATION OF PHYTODETRITUS ON THE SEA FLOOR

biodeposit.” In shallow waters, thorough mixing followed by stabilisation can lead to the deposition of non-motile cells and aggregates. For example, the deposition of Phaeocystis sp. ﬂuff was observed on a tidal cycle during slack water in the North Sea (Williams et al. 1998), and in the Baltic Sea, a storm occurred 4 days before the pulsed sedimentation of the spring diatom bloom in 1980 (Graf et al. 1982). In stratiﬁed systems, the mixed layer may deepen, entraining water from deeper in the water column. Stabilisation, or the shallowing of the mixed layer, leads to detrainment, which effectively enhances sedimentation through the net downward transport of phytoplankton and aggregates (now below the mixed layer). Such vertical displacement will lead to the deposition of phytodetritus following episodic events such as storms. For example, at the abyssal BIOTRANS site, Pfannkuche et al. (1999) suggested that an early sedimentation event in March 1992 was associated with frequent gales. Potentially, this process may be linked to the phytodetritus observed on the sea ﬂoor in the western Arabian Sea in March 1995 (Pfannkuche et al. 2000), when an export peak associated with strong winds “arrived immediately in the interior” (in sediment trap MS-4; Honjo et al. 1999). Mixed layer effects may be seasonal where there is seasonal shallowing of the mixed layer and development of a seasonal thermocline. Such a process may be important for the deposition of spring diatom blooms, for example at the Porcupine Abyssal Plain site with a deep mixed layer in winter (Rice et al. 1994, Lampitt et al. 2001). In addition, mixed layer effects may be responsible for a “fall dump” of large, “shade ﬂora” diatoms that live near the seasonal thermocline (Kemp et al. 2000). The “fall dump” as deﬁned by Kemp et al. (2000) is the sedimentation “. . . of a long-lived episode of production (lasting the duration of the seasonal thermocline) and triggered by the fall/winter mixing that breaks down stratiﬁcation.” Although contributing to the geologic record in laminated sediments, “fall dumps” do not appear to be associated with any ﬂuff layers in Table 1. The seasonal thermocline, in general, can enhance the aggregation of phytoplankton into marine snow (e.g. Macintyre et al. 1995). For example, Billett et al. (1983) suggested, “This process . . . is supported by diver observations in the Sargasso Sea where detrital material collecting at the thermocline has been seen to peel off and sink in strings up to 15 cm long.” The aggregation of a massive Ceratium bloom at the thermocline over a large area in the New York Bight occurred days before a ﬂuff layer was deposited (Mahoney & Steimle 1979).

Mesoscale eddies In addition to wind-mixing events, Conte et al. (2001) suggested that episodic high ﬂuxes into sediment traps in deep water off Bermuda may be enhanced by the passage of mesoscale eddies. Mesoscale eddies are rotational features of the upper water column with length scales of 50–200 km, lasting on the order of months to a year, travelling a few kilometres per day. They may be cyclonic, cold-core eddies or anticyclonic, warm-core eddies. Cold-core eddies, in particular, enhance production by inducing the upwelling of nutrients into surface waters. These eddies enhance aggregation through this increase in the abundance of phytoplankton. Sedimentation may be enhanced locally through bursts of downwelling vertical movement. Thiel et al. (1988/89) were ﬁrst to suggest that cold-core eddies trigger the deposition of phytodetritus to the sea ﬂoor at the abyssal BIOTRANS site. Cyclonic eddies were reported in this area during the Joint Global Ocean Flux Study (JGOFS) North Atlantic Bloom Experiment in spring/summer 1989 (NABE; Robinson et al. 1993). 203

STACE E. BEAU L I E U

Mesoscale variability in vertical ﬂux was indicated by a pulse in August 1989 collected in one set of sediment traps 60 km north of BIOTRANS (Newton et al. 1994) but not in sediment traps 100 km to the west (Honjo & Manganini 1993). At this same time, Pfannkuche (1993) reported a phytodetrital layer 60–80 km north of the BIOTRANS site but not at BIOTRANS itself. Cold-core eddies may trigger sedimentation of phytodetritus at other sites in the northeast Atlantic, for example in the Iceland Basin where the deep-sea nepheloid layer was enriched in compounds speciﬁc to Emiliania huxleyi 1 month after blooms were observed in cold-core eddies in surface waters (Conte et al. 1995). Eddies may be more likely in a particular season, for example when associated with coastal jets. Seasonal wind-forced coastal jets can develop meanders and shed eddies to deeper waters (e.g. Strub & James 2000). For example, Honjo et al. (1999) suggested that the shelf and basin environment in the Arabian Sea are coupled through the advection of eddies originating near the Omani coast during monsoon seasons. Mesoscale eddies occurred concurrently with several large export pulses to sediment traps moored in the Arabian Sea during both the northeast and southwest monsoon in 1994 –95 (Honjo et al. 1999). The pulses associated with eddies had elevated biogenic silica to calcium ratios, suggesting that the eddies enhanced production or at least the export ﬂux of diatoms (Honjo et al. 1999). The northeast monsoon eddy event in October–November 1994 was perhaps associated with the collection of phytodetritus on the sea ﬂoor at this time in the western Arabian Sea (L. Levin, pers. comm.). Similar eddies may link the shelf and slope environment off the California coast, where the observation of pulsed sedimentation at Station M was linked temporally to the Bakun upwelling index, computed from barometric pressure distributions (Baldwin et al. 1998, K. Smith et al. 2001). From 1990–98, ﬂuff layers were observed on the sea ﬂoor at Station M during years in which the maximum Bakun upwelling index was >60 m3 s−1 (100 m of shoreline)−1. In addition, both sites off California in Table 1 are in close proximity to coastal headlands that may direct the coastal jet offshore (Point Conception and Cape Mendocino). With data from satellite altimetry, such as Topex/Poseidon, we can now look for spatial and temporal correlation between cyclonic eddies in surface waters and phytodetritus on the sediment surface at time-series sites such as BIOTRANS, WAST and Station M.

Fronts Fronts are the convergence zones between water masses and are relatively narrow regions characterised by large gradients in physical properties such as temperature or salinity. The cross-frontal transport of nutrients enhances phytoplankton production (e.g. Mann & Lazier 1991). Convergent ﬂows accumulate high concentrations of particles and enhance the aggregation of phytoplankton. Fronts also enhance sedimentation through downwelling vertical velocities associated with the convergence. Examples of such convergence zones include upwelling fronts, which develop offshore due to Ekman transport during coastal upwelling (Mann & Lazier 1991). An upwelling front perhaps contributed to the deposition of phytodetritus at a site on the Chilean margin, inﬂuenced by the Peru, or Humboldt, Current (L. Levin, pers. comm.). Other examples include shelf-break fronts, marking the transition from shelf waters to warmer, more saline slope waters (Mann & Lazier 1991). Thiel et al. (1988/89) pointed to the shelf break, associated with high phytoplankton biomass, as contributing to the deposition of phytodetritus in the Porcupine Seabight. Processes at 204

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the shelf-break front south of Georges Bank may have contributed to the deposition of phytodetritus at two sites on the northwest Atlantic slope (Hecker 1990). Deposition of phytodetritus in the abyssal equatorial ocean was linked to a very large-scale oceanic front, observed as a “line in the sea” with very high concentrations of the diatom Rhizosolenia sp. (Yoder et al. 1994). Tropical instability waves propagated along this front between the south equatorial current and the north equatorial countercurrent during the few months before phytodetritus was observed on the sea ﬂoor in 1992 (C. Smith et al. 1996). Deposition of phytodetritus in this region was also observed years before, but was probably not associated with tropical instability waves (Gardner et al. 1984). Other large-scale fronts that appeared to be associated with phytodetritus on the sea ﬂoor are the Subantarctic Front and Polar Front in the Southern Ocean. For example, the northern and southern sites listed for the ﬂuff layers sampled by Mackensen et al. (1993) lie under these fronts. The region between these fronts, the Polar Frontal Zone, is known for mesoscale cyclonic eddies (Open University Course Team 1989) and vast deposits of opal on the sea ﬂoor (Lampitt & Antia 1997). Not surprisingly, ﬂuff comprised mainly of diatoms was sampled in this region and just south of the Polar Front by Riaux-Gobin et al. (1997) and Martin & Sayles (2000). The Polar Front is close to the northern limit of the seasonal ice sheet, and ice edge effects (described below) may contribute to the massive deposition of phytodetritus in this region.

Ice edge effects Grebmeier & Barry (1991) reviewed processes enhancing benthic–pelagic coupling near the ice edge in polar regions. The melting of seasonal ice sheets in polar regions leads to the formation of a layer of low-salinity surface water, temporarily introducing a pycnocline in the water column (e.g. W. Smith & Nelson 1985). Due to the increased stratiﬁcation, phytoplankton are not mixed below the critical depth, and net production is enhanced (W. Smith & Sakshaug 1990). The melting also releases ice algae into surface waters, perhaps seeding ice-edge blooms. The ice edge enhances aggregation through this increase in the abundance of phytoplankton. These ice-edge effects do not directly enhance sedimentation of phytoplankton. However, effects such as the detrainment of the mixed layer after the passage of a storm may be important for the net vertical transport of phytodetritus near the ice edge. For example, in the Southern Ocean in 1991, Gutt et al. (1998) explained, “The exceptionally thick layer of phytodetritus at the three stations in the Lasarev Sea can be explained by an intensive storm 9 to 22 days prior to our observation.” In this case, the storm diluted phytoplankton biomass over the entire water column, and within 2 days of the onset of the storm, pigments in the sediment indicated that 10% of the bloom was deposited (Bathmann 1992, Gleitz et al. 1994). Ice-edge effects were not linked to mass sinking of senescent blooms but rather to multiple, rapid pulses of Phaeocystis sp. that collected temporarily on the sediment surface in the Ross Sea (W. Smith, pers. comm., DiTullio et al. 2000). Gradual accumulation of a phytodetrital layer was observed recently on the shelf near the Antarctic Peninsula (C. Smith, pers. comm.). Ice-edge effects probably contributed to the accumulation of Phaeocystis on the sea ﬂoor in McMurdo Sound (Dayton 1990) and the seasonal observation of phytodetritus on the shelf and slope in the eastern Weddell Sea (Gutt et al. 1998). In the Arctic, ﬁlamentous mats of ice algae, speciﬁcally the diatom Melosira sp., were observed in surface 205

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waters (Ambrose & Renaud 1997, J. Grant, pers. comm.) and were probably responsible for the ﬁrst peak in phytopigments in sediment collected at ∼300 m in the Northeast Water Polynya (Ambrose & Renaud 1997). Graf et al. (1995) also suggested that proximity to the ice edge contributed to the ﬂuff layer sampled in the Greenland–Norwegian Sea in June– July 1989.

Accumulation and persistence on the sea ﬂoor Phytoplankton aggregates sink rapidly during mass sedimentation and allow a coupling between surface water production and deep-sea consumers on the timescale of weeks. Values for settling velocity, or sinking rates, of marine snow were reviewed by Alldredge & Silver (1988) and recently by Diercks & Asper (1997). Generally, the sinking rate of marine snow is 100–150 m day−1 (Conte et al. 2001). Values within this range were calculated for ﬂux to sediment traps at the BIOTRANS site (Pfannkuche 1993) and used for the model of export ﬂux at the Porcupine Abyssal Plain site (Lampitt et al. 2001). Sometimes, even higher sinking speeds were reported for phytoplankton aggregates. For example, a near-surface diatom bloom was observed after 3 wk in a sediment trap at 4000 m at Station ALOHA near Hawaii, indicating sinking speeds of ∼200 m day−1 (Scharek et al. 1999). For a 10-yr time series of particle ﬂux at Station PAPA in the northeast Paciﬁc, Wong et al. (1999) reported sinking rates ranging from 175–300 m day−1 during periods of high productivity. Even faster sinking speeds have been reported for algae packed in faecal pellets (e.g. cyanobacteria in salp faeces; Pfannkuche & Lochte 1993). The entangling of phytoplankton ﬂocs with phaeodarians may have increased the sinking rate of phytodetritus observed at BIOTRANS in July 1986 (Riemann 1989) and at Station M in August–September 1994 (Beaulieu & Smith 1998). Time-series studies of vertical ﬂux to the deep ocean were reviewed by Honjo (1996) and Lampitt & Antia (1997). Since then, several other long-term studies indicated episodic and seasonal pulses as well as interannual variability in ﬂux to the deep sea (e.g. Wong et al. 1999, Rixen et al. 2000, K. Smith et al. 2001). Pulsed sedimentation events are usually short-lived, often collected in only one sediment-trap cup during a time-series deployment, for example the “outstanding export pulse” in July 1995 in the Arabian Sea (POC ﬂux ∼60 mg C m−2 day−1; Honjo et al. 1999). A sediment-trap cup during a long-term deployment will often integrate ﬂux over 1–3 wk. One to two weeks is the timescale for settling of the spring bloom in shallow temperate waters (e.g. Graf et al. 1982). As suggested for the northeast Atlantic by Pfannkuche (1993), pulses “. . . are difﬁcult to predict in time and space but probably represent the maximum seasonal input of [particulate organic matter] into the deep-sea system.” Lampitt & Antia (1997) introduced the Flux Stability Index (FSI), indicating the number of days in one year required for half of the annual ﬂux to be collected by deep sediment traps. Perhaps there is a correlation between regions with low FSI values and the seasonal accumulation of phytodetritus on the sea ﬂoor; all locations listed in Table 2 are in regions with low FSI values (i.e. <3 mo). Sediment traps are known to sample from a “statistical funnel” which incorporates the horizontal dilution of settling particles (Siegel & Deuser 1997). However, estimates of mass ﬂux from sediment traps are usually assumed to be one-dimensional (vertical). Especially near the continental margin, horizontal or lateral transport can supply phytodetritus to the sea 206

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ﬂoor. For example, the thickest ﬂuff layers, 30 cm and 25 cm, respectively, were observed in a submarine canyon in the Porcupine Seabight (Tudhope & Scofﬁn 1995) and in a trough in the Antarctic shelf (Barthel 1997). Thomsen & van Weering (1998) reported downslope transport of phytodetritus in the benthic boundary layer on the continental slope just to the south of the Porcupine Seabight. Downslope transport was also suggested as an important mechanism delivering ﬂuff to a site in the Rockall Trough (Black 2001). Strong currents may prevent the accumulation of a ﬂuff layer on the upper slope in this region of the northeast Atlantic (Bett 2001). True rates of accumulation of phytodetritus on the sea ﬂoor, in terms of mass per unit area per unit time, are not measured but would be a function of supply from vertical and horizontal ﬂux and loss through resuspension, decomposition, consumption, and burial (pathways for fate described below). Direct observations, by divers and through time-lapse photography, revealed that phytodetritus can accumulate very rapidly on the sea ﬂoor in pulses lasting on the order of days to weeks. For example in the Porcupine Seabight at 2000 m, a ﬂuff layer developed in ∼2 days in April 1982 (Billett et al. 1983). At greater depth (4025 m) in the Porcupine Seabight in 1983, the sea bed was covered ∼2 wk after the initial arrival of phytodetritus in mid-June, and additional pulses of detritus supplemented the layer through July (Lampitt 1985). At the Porcupine Abyssal Plain site, accumulation to maximum coverage in 1991, 1993, and 1994 occurred over 2–3 wk, starting in late May to mid-June (Bett et al. 2001). In 1991 additional pulses of phytodetritus over a 2-month period caused several peaks in coverage of the sea ﬂoor (Bett et al. 2001). At Station M at 4100 m in the northeast Paciﬁc, the sediment surface was covered by a ﬂocculent “carpet” of phytodetritus during three separate, week-long pulses in summer 1994 (Beaulieu & Smith 1998). In contrast, time-series photographs indicated that phytodetritus accumulated in a more continuous manner, gradually over weeks, at a site on the Antarctic shelf in austral summer 2001 (C. Smith, pers. comm.). Very few studies report direct observations of the persistence, or residence time, of phytodetritus on the sea ﬂoor. The residence time of individual phytodetrital aggregates in time-lapse photographs of the deep-sea ﬂoor was only on the order of days in the Porcupine Seabight and at Station M (Lampitt 1985, K. Smith et al. 1994). The total time for signiﬁcant coverage of ﬂuff layers includes accumulation, continued deposition, and degradation (unless the layer is resuspended as often occurs at shallow water sites). The residence time of ﬂuff layers in shallow waters ranged from 1–2 days at energetic sites (Witt et al. 2001) to 1–2 wk (Davies 1975, Mahoney & Steimle 1979, Graf et al. 1982, K. Kormas, pers. comm.) to 1–3 months at a depositional site (Witt et al. 2001). Excluding the “permanent” ﬂuff layer in the Black Sea (Pilskaln & Pike 2001), ﬂuff layers in the deep sea tended to last on the order of weeks to a couple months. Indirect evidence from sampling on successive cruises indicated ∼2 wk for the residence time of a ﬂuff layer at 1920 m in the Rockall Trough (Black 2001). Video recordings at the sea-ﬂoor observatory at 1177 m in Sagami Bay showed a ﬂuff layer present for 2–3 months in 1997 and 1998 (Kitazato et al. 2000). Time-series photographs at 4025 m in the Porcupine Seabight showed signiﬁcant coverage of phytodetritus for at least 2 months in 1983 (Lampitt 1985). At the Porcupine Abyssal Plain (PAP) site in 1991 and 1993, a ﬂuff layer covered a signiﬁcant area of the sea ﬂoor for ∼4 months (Bett et al. 2001). A ﬂuff layer did not develop at PAP in 1997–99, although individual aggregates were observed temporarily on the sea ﬂoor during summer (Bett et al. 2001). At Station M in 1994, ﬂocculent phytodetritus was deposited in three pulses (described above) and coated the sediment surface for ∼4 months (Lauerman & Kaufmann 1998). However, patches 207

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of phytodetritus (“rad patches”) persisted through February 1995, when the material resembled the “benthic ﬂoc” described by Reimers & Wakeﬁeld (1989).

Impact on ﬂuxes between sediment and water column With residence times in the order of days to months, ﬂuff layers are rather ephemeral features of the sediment/water interface. This section refers to the few studies that indicate the impact of phytodetritus on ﬂuxes at the sea ﬂoor. More studies are needed to evaluate the impact of these unique interfaces on ﬂuxes of dissolved and particulate matter between the underlying sediment and the overlying water column. In situ studies of such ﬂuxes can be done with instruments that enclose a small area of the sediment surface and volume of overlying water, such as benthic incubation chambers (e.g. Tengberg et al. 1996) or ﬂumes (e.g. Amos et al. 1992). Fluxes of dissolved compounds may also be calculated from porewater proﬁles. Instruments such as microelectrode probes (e.g. Luther et al. 1999) and planar optodes (e.g. Jorgensen et al. 2001) can be used to proﬁle through a ﬂuff layer and into underlying sediment.

Fluxes of solutes and gases Fluxes of dissolved substances from the sediment to the water column are generally dependent on the remineralisation of biogenic particles at the sediment/water interface (Reimers 1989). Benthic ﬂuxes tend to be dominated by “recently-settled, reactive detritus” composed of organic, opaline, and calcareous materials (Reimers 1989). As a speciﬁc example, Martin & Sayles (2000) measured pore-water proﬁles along a longitudinal transect in the Southern Ocean and found, “Sedimentary recycling of biogenic silica and organic carbon was most rapid and variable at the three sites where a ﬂuff layer was clearly visible on the sediment surface.” In a Finnish lake following the deposition of spring bloom diatoms, the concentration of biogenic silica in pore waters was enough to mobilise phosphorus from the sediment (Tallberg 1999). However, remineralisation may be slowed sometimes by the chemical microenvironment that can develop within patches of phytodetritus. As described by Reimers (1989), when phytodetritus is concentrated in “traps” on the sea ﬂoor such as in relict burrows, “localised solute concentrations may rise above those in surrounding sediments, retarding solid dissolution rates.” Indirect evidence for a chemical microenvironment within a ﬂuff layer included a correlation between degree of pigment preservation and thickness of the ﬂuff sampled in the Southern Ocean (Riaux-Gobin et al. 1997). Chemical microenvironments in marine snow aggregates have been measured directly with microelectrodes (e.g. Alldredge & Cohen 1987). The remineralisation of organic carbon utilises oxygen and might lead to a sub- or anoxic microenvironment within phytodetritus. A laboratory study in which phytodetritus was added to the surface of sediment cores revealed immediate yet temporary increases in benthic O2 consumption and total CO2 and dissolved organic nitrogen efﬂux (Hansen & Blackburn 1992). This same study also reported a temporary (weeks) increase in anaerobic respiration (sulphate reduction and denitriﬁcation; Hansen & Blackburn 1992). Similarly, in situ at Station M at 4100 m in the northeast Paciﬁc, tube core respirometers were placed over 208

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detrital aggregates, and the results indicated that both oxygen and nitrate were used in respiration (Wolgast et al. 1998). Fluff layers have been observed to lower oxygen concentrations in bottom waters as well as in underlying sediment. As an extreme example, a ﬂuff layer led to anoxic bottom waters over 15 000 km2 in the New York Bight (Mahoney & Steimle 1979). When phytodetritus accumulates at the sediment surface, oxygen concentrations in pore waters tend to be depleted, consequently raising the horizon of the redox potential discontinuity (RPD) within the sediment (Graf 1992). This effect was observed in multiple-corer samples from the abyssal equatorial Paciﬁc, in which “The amount of fast-reacting organic carbon in a core, as indicated by its pore-water oxygen proﬁle, was signiﬁcantly correlated with the amount of phytodetritus observed on the core top” (C. Smith et al. 1997). Also, the position of the RPD migrated on a seasonal cycle in Sagami Bay in response to the ﬂuff layer that accumulated each spring (Ohga & Kitazato 1997, Kitazato et al. 2000). Elevating the RPD proﬁle within the sediment has important effects on the ﬂuxes of redox-sensitive metals. Jago et al. (1993), describing a study in the North Sea, stated that . . . deposition of organic-rich detritus gave rise to seabed anoxia and efﬂux of trace metals (Fe and Mn) from pore waters. Settling, deposition, and resuspension of ﬂuff were therefore important controls of metal exchanges in the boundary layer. In addition, the accumulation of detritus in patches on the sea ﬂoor could result in heterogeneous ﬂuxes of redox-sensitive metals over small (<1 m) spatial scales (Reimers 1989). Such heterogeneity was suggested for a site in the abyssal northwest Atlantic, in which the ﬂuxes of Mn and Fe were enhanced by phytodetritus that accumulated in relict burrows (Aller & Aller 1986).

Particulate ﬂuxes Fluff layers have been described as hydrodynamically coupled to the water column (Stolzenbach et al. 1992). A porous interfacial layer may be agitated and even ﬂushed by ﬂow in the overlying water. An extreme example of such hydrodynamic coupling occurs in the “permanent” ﬂuff layer in the Black Sea, in which heavier faecal pellets are sorted below lighter coccoliths (Pilskaln & Pike 2001). For all ﬂuff layers, hydrodynamic coupling may enhance the deposition of ﬁne particles, such as contaminants, to the sea bed (Stolzenbach et al. 1992). Such a process was responsible, perhaps, for the elevated concentrations of saturated organic compounds in the interfacial ﬂoc, the top 2 mm, of the ﬂuff layer in the Black Sea (Beier et al. 1991). In addition to enhancing the deposition of suspended particulate matter, a ﬂuff layer may promote the recruitment of planktonic larvae to the sea ﬂoor through a behavioural response. Phytodetritus may be an important control of particle size spectra in the benthic boundary layer (e.g. Thomsen & van Weering 1998). Although ﬂuff layers usually are ﬂocculent and easy to resuspend as described below, some gelatinous layers have been sampled with box corers (e.g. Pilskaln 1991, de Wilde et al. 1998) and perhaps were “armouring the bed” (i.e. prohibiting the resuspension of sediment). Indeed, Lampitt et al. (2000) suggested that particulate ﬂuxes into the benthic boundary layer just north of the BIOTRANS site might have been restricted in summer 1989 and 1990 by phytodetritus blanketing the sea ﬂoor. 209

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Fate of phytodetritus on the sea ﬂoor The ultimate fate for organic carbon that reaches the sea ﬂoor is usually remineralisation, with a small fraction stored in the sediment. Carney (1989) presented a scheme for the partitioning of detritus at the deep-sea ﬂoor that included the benthic boundary layer above the sediment/water interface, a sediment mixed layer and a fossil layer. Remineralisation of labile organic matter generally occurs at or near the sediment/water interface on short timescales of days to weeks (e.g. Carney 1989, Turley & Lochte 1990, Conte et al. 1995). Further remineralisation of refractory organic matter takes places on longer timescales in the sediment mixed layer (years) and fossil layer (hundreds of years). Eventually, the storage of carbon in the fossil layer, representing a geological record of production exported to the sea ﬂoor, tends to be a very small percentage of the initial input (e.g. Carney 1989, Lampitt et al. 2000), although the overall range for burial efﬁciencies is <1–70% (Henrichs 1992). An interesting question is whether the accumulation of phytodetritus on the sea ﬂoor is associated with higher burial efﬁciencies. This hypothesis probably would not be true for shallow areas that act only as temporary “depocenters” before advection of the organic matter to deeper waters (e.g. van Raaphorst et al. 1998). Furthermore, in depositional areas in the deep sea, benthic organisms may be adapted to processing the episodic downpours of phytodetritus (e.g. Linke et al. 1995). Below, four major pathways for the fate of phytodetritus on the sediment surface are outlined: a) resuspension, b) decomposition by bacteria, c) consumption by benthic fauna, and d) incorporation into underlying sediment. The discussion focuses on short time-scale processes at and near the sediment/water interface.

Resuspension Phytodetritus on the sea ﬂoor may be resuspended into the water column by an increase in near-bottom ﬂow speed. The resuspension of phytodetritus from the deep-sea ﬂoor ﬁrst was observed in time-lapse photographs by Lampitt (1985) and recognised as “rebound” particles by Walsh et al. (1988). Using terms from sediment transport theory, resuspension requires that ﬂow in the benthic boundary layer induces a bed shear stress that exceeds the critical shear stress for phytodetritus. Often, the critical shear stress (τ0crit) is parameterised in terms of critical shear velocity (u*crit), deﬁned by the following equation in which ρ is ﬂuid density: τ0crit = ρu*crit2. The critical shear velocity may be related to the mean ﬂow speed at the top of the benthic boundary layer through a drag coefﬁcient (Cd). Lampitt (1985) determined that phytodetritus in the Porcupine Seabight resuspended when ﬂow speeds exceeded 7 cm s−1 (Lampitt 1985). With a typical drag coefﬁcient (Cd = 0.0025), u*crit was 0.35 cm s−1 for this observation. This u*crit value was corroborated by other indirect estimates for the resuspension of shallowwater (e.g. 0.3 cm s−1; Williams et al. 1998) and deep-sea phytodetritus (e.g. 0.4–0.7 cm s−1; Beaulieu & Baldwin 1998). Recent, direct measurements of u*crit for diatom-derived phytodetritus ranged from 0.4–0.8 cm s−1 (S. Beaulieu, unpub. data). Overall, this range for u*crit (0.3–0.8 cm s−1) corresponds to mean ﬂow speeds of 6–16 cm s−1 for resuspension of phytodetritus (using Cd = 0.0025). Flow speeds less than these values might induce bedload 210

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transport of phytodetritus. For example, observers in submersibles “. . . noted aggregates of surface ﬂuff rolling in tumbleweed fashion along the sea bed . . .” (Jumars et al. 1990). Resuspension affects the fate of phytodetritus by decreasing the probability of burial in the sediment, inﬂuencing the ultimate site of deposition, and redistributing organic matter to consumers. By decreasing the probability of burial, resuspension basically extends the time during which phytodetritus is exposed to decomposition in the water column and at the sediment/water interface (Sanford 1992). In this sense, resuspension promotes the remineralisation of organic carbon. By inﬂuencing the ultimate site of deposition, resuspension determines the actual site at which remaining carbon may be stored. On a large scale, resuspension and lateral transport of phytodetritus are important processes in shallow waters and at the continental margin, advecting organic carbon to deeper areas. Such lateral transport appears to be important in the Baltic Sea (Graf et al. 1982, Witt et al. 2001) and the North Sea (van Raaphorst et al. 1998), as well as in deeper locations of the northeast Atlantic continental margin (e.g. Bett 2001). Recent evidence from instruments that collected water samples within the benthic boundary layer indicated that phytodetrital aggregates were advected very near the bed rather than whisked high into the water column in the shallow (Leipe et al. 2000) and deep sea (Thomsen & van Weering 1998). This transport in proximity to the bed has important implications for the redistribution of phytodetritus as food for benthic suspension feeders (e.g. Grant et al. 1997). Small-scale heterogeneity (patchiness) of phytodetritus on the bed is enhanced through redeposition around topographic features and in depressions on the sea ﬂoor (e.g. Thiel et al. 1988/89, Carney 1989, Graf et al. 1995). Bedload transport may contribute also to localised focusing of phytodetritus in depressions. Such deposition and bedload transport redistributes food to deposit feeders, for example in feeding pits (Yager et al. 1993).

Decomposition by bacteria Although the organic matter in marine snow decomposes during settling through the water column (e.g. Kiørboe 2001), the short residence time of phytodetrital aggregates on the sea ﬂoor implies that decomposition occurs much more rapidly at the sediment/water interface. As stated by Jumars et al. (1989), “Clearly, the mechanism of turnover of incoming ﬂux at the sea ﬂoor is not merely an extension of that in the water column.” Such observations point to a very rapid response of the benthic community to the deposition of phytodetritus (e.g. Gooday & Turley 1990, Pfannkuche 1993). In particular, bacteria from the underlying sediment or from the overlying benthic boundary layer may colonise phytodetritus on the sea ﬂoor and decompose the majority of the organic matter. This section and the next are brief updates to the review by Gooday & Turley (1990). This section summarises recent observations of bacterial biomass and activity in phytodetritus collected from the sea ﬂoor. Bulk measures of phytodetrital decomposition are included, such as sediment community oxygen consumption (SCOC). Microbial biomass in phytodetritus is often higher than in the equivalent mass of underlying sediment (C. Smith 1994), with some exceptions (e.g. Thiel et al. 1988/89). The number of bacteria, size, and frequency of dividing cells in phytodetritus may be determined directly through counts under an epiﬂuorescence microscope. For phytodetritus sampled from the deep sea, heterotrophic bacteria tended to be on the order of 1010 cells g−1 dw (107 cells ml−1; Rice et al. 1986, Lochte & Turley 1988, Beaulieu & Smith 1998). In ﬂuff 211

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sampled from the Rockall Trough in summer 1997 and 1998, bacteria were six times more abundant than in underlying sediment (Black 2001). New methods allow quantiﬁcation of bacterial biomass (which can be converted to bacterial abundance) without the tedium of microscope counts. For example, bacteria-derived biomarkers, such as bacteria-speciﬁc organic compounds (odd and branched chain fatty acids and hopanoids), were at high concentrations in a core taken from 2400 m in the Iceland Basin following a pulse of phytodetritus (Conte et al. 1995). Another new technique that provides not only a measure of bacterial biomass but also a proﬁle that can be used to “ﬁngerprint” microbial community composition is phospholipid fatty acid analysis (PLFA; Green & Scow 2000). Although PLFA has not been used (yet) for quantifying bacteria in natural samples of phytodetritus, the method was used recently in an in situ tracer uptake experiment (explained below; Moodley et al. 2001). Less speciﬁc, bulk measures of mainly bacterial biomass include the concentrations of ATP, DNA, and RNA (Table 3). For example in the Baltic Sea, ATP biomass (assumed to be bacteria and protozoans) in the surface sediment increased rapidly (∼1 wk) in response to a pulse of phytodetritus, but “disappeared” soon afterwards, probably due to consumption by larger organisms (Graf 1992). At the abyssal BIOTRANS site, ATP biomass in the surface sediment had a distinct seasonal maximum in July–August, ∼1 month after the seasonal maximum in chl a in the surface sediment (Pfannkuche 1993). Speciﬁcally for phytodetritus sampled from the northeast Atlantic slope, de Wilde et al. (1998) stated, “The high RNA, DNA concentrations indicate enhanced biomass of microbiota in the mucus layer.” The ratio of RNA to DNA concentration can be used as a relative indicator of microbial activity. At one of the sites on the northeast Atlantic slope at 3650 m, the RNA/ DNA ratio in phytodetritus was very high (0.78) compared with the upper sediment when the mucus layer was not present (0.08; de Wilde et al. 1998). Studies have shown that barophilic bacteria colonising phytodetritus are capable of rapid utilisation of the labile organic matter. Estimates of bacterial exoenzyme activity, growth rate, and production in phytodetritus require incubation experiments in situ or at simulated pressure and temperature. During shipboard incubations of sediment from the abyssal BIOTRANS site enriched with sterilised net plankton, the activity of hydrolytic exoenzymes increased within days (Boetius & Lochte 1994) and 10% of the total organic carbon was utilised in <3 days (Turley & Lochte 1990). In one of the ﬁrst studies of bacterial growth in deep-sea phytodetritus, Lochte & Turley (1988) observed rapid growth during the ﬁrst 2 days, with up to 10% dividing cells. After 10 days, the total number of bacteria had decreased while the number of barophilic ﬂagellates, which fed on bacteria, had increased. More recent studies of bacterial growth in deep-sea phytodetritus involved tracer uptake experiments with labelled amino or nucleic acids. For example, bacteria in phytodetritus sampled from the abyssal equatorial Paciﬁc, incubated with 14C-glutamate, exhibited uptake rates more than ﬁve times higher than underlying sediment (C. Smith et al. 1996). Also, for samples from the Rockall Trough in summer 1997 and 1998, doubling times for bacteria in the ﬂuff were 2–4 days, with 3H-thymidine incorporation rates twenty times faster than underlying sediment (Black 2001). Bulk measures of (mostly microbial) metabolic activity include electron transport system activity (ETSA) and the calorimetric measurement of heat production (heat ﬂux). Generally, there was a very quick (<1 wk) response, indicated by increased ETSA and heat ﬂux, to deposition of phytoplankton blooms in shallow water environments (reviewed by Graf 1992), with some exceptions (e.g. Czytrich et al. 1986). ETSA did not, however, have a seasonal pattern at the abyssal BIOTRANS site (Pfannkuche 1993). 212

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Sediment community oxygen consumption (SCOC) is another bulk measure of (mostly microbial) metabolic activity. Assuming a respiratory quotient, the oxygen consumption may be converted to organic carbon respiration. In shallow water and in experimental laboratory simulations, SCOC often exhibited a very quick and short-lived response (∼1 wk) to the deposition of phytodetritus to the sediment (reviewed by Graf 1992). K. Smith & Baldwin (1984) ﬁrst reported a seasonal change in SCOC in the deep sea at two abyssal sites in the Paciﬁc. For the abyssal BIOTRANS site, Pfannkuche (1993) suggested that bacteria and protozoans colonising “the epibenthic phytodetrital layer were responsible for 60–80% of the seasonal increase in SCOC.” In September 1994 at Station M in the northeast Paciﬁc, K. Smith et al. (1998) used a submersible to place tube core respirometers over detrital aggregates and found that the SCOC of sediment covered by phytodetritus was signiﬁcantly higher than background sediment (154 compared with 136 nmol O2 cm−2 day−1). A summary of respiration measurements made speciﬁcally on deep-sea detrital aggregates using four laboratory methods, suspended core incubations, and in situ SCOC is in table 5 of K. Smith et al. (1998). Measurements of the decomposition of organic matter, such as SCOC, may be aliased by the episodic and ephemeral nature of phytodetritus on the sea ﬂoor. Controlled, in situ experiments with deliberate tracers have been done to understand better the initial decomposition of organic matter at the sediment/water interface. Tracer experiments at 2000 m in the Bay of Biscay revealed the quick response (<1 day) of bacteria to dissolved 14C-labelled sugars and amino acids that were injected by a submersible into box corers (Cahet & Sibuet 1986). Replication of the experiments revealed higher bacterial utilisation in June 1987, when phytodetritus was present on the sea ﬂoor, than in August 1986 (Cahet et al. 1990). Recently, in situ tracer experiments were done with autonomous landers. For example, Moodley et al. (2001) studied the short-term fate of phytodetritus by seeding a benthic chamber with 13C-labelled algae, monitoring the respiration of the 13C, and tracing the transfer of 13C from phytodetritus to bacterial biomarkers (PLFA) and benthic organisms. As another example, Witte et al. (2001) studied the response of the benthic community at the Porcupine Abyssal Plain site to a pulse of 13C/15N-labelled algae, and found a 40-fold increase in the activity of bacterial exoenzymes and a doubling of SCOC within days.

Consumption by benthic fauna Different groups of the sediment community respond in different ways and on different timescales to the deposition of phytodetritus (Pfannkuche et al. 2000). As described above for bacteria, even in the deep sea benthic fauna may respond quickly to the input of labile organic matter. As concluded by Levin et al. (1999), “. . . we infer that phytodetritus reaching the seabed in margin environments is rapidly processed by the protozoan and metazoan components of the benthic fauna.” Functional responses by some protozoan and metazoan taxa, such as increased foraging activity, contribute within days to the recycling of phytodetritus at the sea ﬂoor. Below, the discussion focuses on the direct consumption of phytodetritus by three size groups of organisms, the meio-, macro-, and megafauna. Although microbes generally are responsible for the majority of the remineralisation of organic carbon at the sea ﬂoor, in some cases these larger organisms consume a large percentage of the fresh phytodetritus. When fresh phytodetritus arrives at the sea ﬂoor, “. . . metazoan deposit feeders will be competing directly with rapidly growing microbes for 213

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labile [particulate organic material]” (C. Smith 1994). For some benthic fauna, however, phytodetritus as a food source may be enhanced by the presence of bacteria (e.g. Jumars et al. 1990).

Meiofauna Meiofauna, or meiobenthos, are the Protozoa and small-sized (i.e. <300 µm) Metazoa that live on and in the sediment. Laboratory and in situ tracer experiments indicated that some meiofaunal taxa rapidly ingested organic carbon deriving from phytodetritus. In laboratory and mesocosm simulations of the deposition of phytodetritus, uptake of 14C-labelled phytoplankton after 1 month varied greatly among meiobenthic taxa (Rudnick 1989, Widbom & Frithsen 1995, Olafsson et al. 1999). In particular, during a simulation of spring bloom deposition in the Baltic Sea, uptake was highest in a single ostracod species, followed by nematodes (Olafsson et al. 1999). In shorter incubations, in situ tracer experiments (described above) indicated uptake of labelled organic matter by meiofauna as a group within days (Cahet & Sibuet 1986, Moodley et al. 2001). Some meiofauna, including foraminiferans and nematodes, have been found in great abundance in deep-sea phytodetritus compared with underlying sediment (Thiel et al. 1988/89, Black 2001). Opportunistic species may be able to colonise phytodetrital aggregates as an ephemeral habitat and food resource (Gooday & Turley 1990). For example, a species of nematode that directly ingested small algal cells may have reproduced within gelatinous aggregates of phytodetritus sampled from BIOTRANS site (Thiel et al. 1988/89, Riemann 1995). However, metazoan meiofauna usually do not respond numerically within a short time following the deposition of phytodetritus (e.g. Gooday et al. 1996), with the exception of meiofauna studied in a lake with spring diatom deposition (Goedkoop & Johnson 1996). In particular among the meiofauna, some foraminiferans appear to respond quickly to the deposition of phytodetritus (reviewed in Gooday & Turley 1990, Gooday et al. 1992a, and Gooday & Rathburn 1999). As reported by Gooday & Rathburn (1999), foraminiferans that respond to phytodetritus are mainly epifaunal or shallow infaunal opportunists, representing a small fraction of the total number of foraminiferal species in the sediment. In particular, Alabaminella weddellensis and Epistominella exigua, two species that were very abundant in phytodetritus from two deep-sea sites in the northeast Atlantic, ingested small algal cells (Gooday 1988, Gooday 1993). Larger species of Foraminifera, such as Bathysiphon ﬁliformis, may also be primary consumers of phytodetritus (Gooday et al. 1992b). A variety of phytoplankton are listed as food items for foraminiferans collected from the shallow and deep sea (Gooday et al. 1992a). However, results from a mesocosm simulation of a shallowwater system indicated that foraminiferans “showed a remarkably low preference for fresh detritus” (Widbom & Frithsen 1995). In the deep sea Foraminifera and other agglutinated Protozoa perhaps outcompete metazoan meiofauna for the ephemeral food resource, through more rapid uptake (e.g. Gooday et al. 1996). On the continental slope off Cape Fear in the northwest Atlantic, an in situ tracer experiment with 13C-labelled diatoms indicated that over half of the agglutinated Protozoa sampled 1–1.5 days after the enrichment were labelled with 13C (Levin et al. 1999). Recent tracer uptake experiments at other sites in the deep sea showed that after 1 day Foraminifera consumed as much of the label as bacteria (Moodley et al. 2001). In experiments conducted shipboard with multiple corer samples, the ATP content in one deep-sea foraminiferal species increased 3-fold just 3 days after enrichment with autoclaved net plankton (Graf & Linke 1992). The quick (<1 month) numerical response 214

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of some foraminiferal taxa to the deposition of phytodetritus can lead to increased relative abundance of Foraminifera among the meiobenthos (e.g. Drazen et al. 1998). A recent study in Sagami Bay demonstrated the seasonal increase in the populations of shallow infaunal opportunists as well as deeper-living Foraminifera in response to the ﬂuff layer on the sea ﬂoor (Kitazato et al. 2000).

Macrofauna Macrofauna are the epi- and infaunal organisms retained on a 0.3-mm screen but not conspicuous in photographs of the sea ﬂoor (Gage & Tyler 1991). Macrofauna inﬂuence the fate of phytodetritus through direct consumption and through diffusive and advective mixing into the sediment (described in the next section). Analyses of gut contents as well as tracer uptake experiments indicated that some macrofauna ingested and assimilated organic matter from phytodetritus. For example, the composition of phytodetrital aggregates matched gut contents of polychaetes at BIOTRANS in March 1992 (Pfannkuche et al. 1999) and at Station M in summer 1994 (Beaulieu & Smith 1998). Tracer uptake experiments done in situ with benthic landers at various depths indicated that after 1 day, more 13C-labelled phytodetritus was transferred to macrofauna than to meiofauna (Moodley et al. 2001), although this observation may be due to more gut contents in the larger organisms. Uptake of 14C-labelled phytodetritus in a mesocosm simulation of a shallow-water system was highest in suspension feeders and surface deposit feeders with immediate access to fresh detritus (Widbom & Frithsen 1995). In this 5-month experiment, very few species showed a quantitative increase in biomass or abundance in response to the increased input of phytodetritus, although most taxa showed a preference for fresh detritus as a food resource (Widbom & Frithsen 1995). Tracer experiments conducted in situ on the North Carolina slope revealed that phytodetritus as an immediate food resource was not restricted to macrofauna in the surface (0–1 cm) sediment (Blair et al. 1996, Levin et al. 1997, Levin et al. 1999). 13C-labelled algae were deposited by a submersible onto sediment at two sites at 850 m (off Cape Fear and Cape Hatteras; Levin et al. 1999). Among the macrofauna, annelids were the most active consumers of labelled diatoms at both sites after 1.5 days, including some deep-dwelling (5–10 cm) polychaetes (Levin et al. 1999). In particular at the site off Cape Hatteras, maldanid polychaetes (Praxillella sp.) rapidly subducted 13C-labelled diatoms to depths of ∼10 cm in the sediment through nonselective transport, or “hoeing”, into their feeding cavities (within 1.5 days; Levin et al. 1997, 1999). Such rapid subduction was predicted as an optimal strategy for head-down deposit feeders competing with microbes for phytodetritus as a food source (C. Smith 1994). Blair et al. (1996) calculated vertical transport into the sediment of freshlydeposited diatoms at ≥3 cm day−1 at this site off Cape Hatteras. These results implied that the short residence time of phytodetritus on the sediment surface at this densely-populated site was due to macrofaunal (rather than microbial) activity (Blair et al. 1996).

Megafauna Megafauna are larger organisms (>1 cm) that can be sampled with trawls and/or observed in photographs of the sea ﬂoor. Megafauna affect the fate of phytodetritus through consumption, caching, and mixing into the sediment (described in the next section). Epibenthic megafauna are observed often in (and assumed feeding in) patches of phytodetritus. For 215

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example, the holothuroid in Figure 2D (p. 189), a photograph from the shelf near the Antarctic Peninsula, presumably cleared a small area of the phytodetrital “carpet” (C. Smith, pers. comm.). Several photographs of the sea ﬂoor in the deep northeast Atlantic have shown echinoderms situated in patches of phytodetritus, suggestive of feeding activity (e.g. Billett et al. 1988, Campos-Creasey et al. 1994). The most comprehensive study of the spatial correlation between deep-sea megafauna and detrital aggregates was done by Lauerman & Kaufmann (1998), who examined nine camera-sled transects taken over a 1-yr period (June 1994–June 1995) at Station M at 4100 m in the northeast Paciﬁc. Lauerman & Kaufmann (1998) found that “. . . spatial distributions of several echinoderm species correlated signiﬁcantly with the distributions of detrital aggregates, but these correlations were not consistent across transects or taxa.” Phytodetritus is found often in the gut contents of suspension- and deposit-feeding megafauna. For example, at the BIOTRANS site in summer 1986, microscopic analyses showed phytodetritus in the gut contents of various megafauna, including anemones and echinoderms (Thiel et al. 1988/89). For gut contents of the sea urchin Echinus afﬁnus sampled from the Rockall Trough in late spring/summer, Campos-Creasey et al. (1994) listed many diatom genera and stated that “the distribution of these species in the gut contents conform to those found in the phytodetritus samples taken in the Porcupine Seabight.” In late summer, dinoﬂagellates were more common in the gut contents of E. afﬁnus from the Rockall Trough, while coccolithophorids were present year round (Campos-Creasey et al. 1994). As summarised by Gooday & Turley (1990), deep-sea echinoderms have been known for over a hundred years to feed on phytoplankton. In particular, deposit-feeding holothuroids can exhibit selective ingestion of organic-rich particles (e.g. Sibuet 1984, Billett et al. 1988) and recently-deposited phytodetritus (e.g. Lauerman et al. 1997, Miller et al. 2000). At Station M in the northeast Paciﬁc, the gut contents of the holothuroid Abyssocucumis abyssorum qualitatively resembled the phytodetritus sampled at the same time during summer 1994 (Lauerman et al. 1997, Beaulieu & Smith 1998). Some studies also reported much higher phytopigment concentrations in the gut contents of deep-sea megafauna relative to surface sediment (e.g. Billett et al. 1988). A recent study in the Santa Catalina Basin at ∼1200 m reported that gut contents of the holothuroid Pannychia moseleyi were enriched 500-fold in chlorophyll a relative to surface sediment (Miller et al. 2000). For samples from the northeast Atlantic at 4500 m in August 1995, HPLC chromatograms of the gut contents of the holothuroid Psychropotes longicauda were identical to those of the phytodetrital layer (Duineveld et al. 1997, de Wilde et al. 1998). Gut contents of megafauna may be analysed for excess activity of radionuclides as an indication of the age of ingested material. Early studies, summarised by C. Smith et al. (1993), showed that particle-associated radionuclides with short half-lives were enriched in the bodies and gut contents of deep-sea deposit feeders. A more recent study by Lauerman et al. (1997), using excess 210Pb/ 234Th ratios, demonstrated the rapid ingestion of phytodetritus by two megafaunal species at Station M, Abyssocucumis abyssorum and Oneirophanta mutabilis. Lauerman et al. (1997) compared the ratio of these radionuclide activities in sediment trap samples with phytodetrital aggregates, gut contents of the two holothuroids, and sediment sampled concurrently from the sea ﬂoor. Setting the age of particles in the sediment trap to 0, the detrital aggregates were <0–20 days old, gut contents were 12– 13 days old, and the surface (0–5 mm) sediment was >100 days old. Using estimates of population density and gut passage time, they further calculated that Abyssocucumis abyssorum alone could process 0.2– 4% of the vertical mass ﬂux during summer 1994. In a recent study 216

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done in the Santa Catalina Basin at ∼1200 m, Miller et al. (2000) also found species of mobile, epibenthic holothuroids, including Pannychia moseleyi mentioned above, with gut contents highly enriched in excess 234Th relative to surface sediment. The ages of gut contents relative to sediment trap material were <0–14 days, similar to the study by Lauerman et al. (1997). Miller et al. (2000), using a much quicker gut passage time, calculated that the dominant surface-deposit feeders in the Santa Catalina Basin were capable of processing 40–50% of the daily vertical ﬂux of excess 234Th during winter 1995–96. Organic-rich phytodetritus with relatively high excess 234Th activity was found deep (17–27 cm) in burrows collected from the abyssal equatorial Paciﬁc in November 1992 (C. Smith et al. 1996). These burrows were probably created by “mega-infauna,” such as echiurans. Stockpiling phytodetritus in deep burrows was suggested as a strategy for deposit feeders competing with microbes for an ephemeral food source (C. Smith 1994). Evidence for the actual caching of phytodetritus at depth in the sediment is limited but may be a feeding strategy for some sipunculids and echiurans (Jumars et al. 1990). When combined with knowledge of feeding ecology and metabolism, observations from time-lapse photographs can be used to estimate the impact of megafauna on the mass balance, or carbon budget, of phytodetritus deposited on the sea ﬂoor. Megafaunal activity may be studied when the bodies, tracks, and/or feeding traces of megafauna are conspicuous in photographs of the sea ﬂoor. In particular, time-lapse photographs indicated that motile epibenthic megafauna played an important role in processing phytodetritus at Station M at 4100 m in the northeast Paciﬁc (K. Smith et al. 1993, 1994, Kaufmann & Smith 1997). In a 3-month period from March–June 1991, the majority of the sediment surface (88%) was traversed, or tracked, by mobile megafauna (K. Smith et al. 1993). Over the 17-month period from February 1990–July 1991, the seven numerically dominant species traversed an area equivalent to the time-lapse camera’s ﬁeld of view about twice per year (Kaufmann & Smith 1997). K. Smith et al. (1994) developed an index for the activity of mobile megafauna based on total area traversed divided by total number of megafauna observed per day and normalised to sea-ﬂoor area. Megafauna, in particular holothuroids, increased their activity and changed their patterns of movement when phytodetrital aggregates arrived on the sea ﬂoor in July 1990 (Kaufmann & Smith 1997). Megafaunal activity generally was twice as great during the time when detrital aggregates were visible on the sea ﬂoor (summer/fall 1990; K. Smith et al. 1994). To calculate the impact of direct consumption by megafauna on POC ﬂux to the sea ﬂoor, K. Smith et al. (1993) assumed that all organic matter encountered was ingested, and then used the population density and oxygen consumption of megafaunal species to calculate the percentage of POC assimilated (respired). Overall, mobile megafauna likely ingested ∼8.7% (and consumed only 1.6%) of the POC ﬂux to the sea ﬂoor in March– June 1991 when aggregates were not present on the sea ﬂoor (K. Smith et al. 1993). A recent analysis of Bathysnap photographs at the Porcupine Abyssal Plain (PAP) site in the northeast Atlantic revealed an increase in epibenthic megafaunal activity (“tracking”) over the last 10 yr coincident with a decrease in the amount of phytodetritus visible on the sediment surface. During the period 1997–2000, megabenthos tracked 100% of the sea ﬂoor in ∼6 wk, compared with 2.5 yr during the period 1991–94 (Bett et al. 2001). In particular, the increases in abundance and tracking of the holothuroid Amperima rosea and the ophiuroid Ophiocten hastatum were linked to the disappearance of the ﬂuff layer that routinely developed at the PAP site in the early 1990s (Bett et al. 2001). These two species apparently were feeding selectively on phytodetritus at the PAP site, based on stable nitrogen isotope analyses and microscopic examination of gut contents (Iken et al. 2001). 217

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Incorporation into underlying sediment In addition to microbial decomposition and faunal consumption, chemical processes including the dissolution of calcium carbonate and biogenic silica act to break down phytodetritus on the sea ﬂoor. Early diagenesis of organic matter, reviewed by Henrichs (1992), is deﬁned as “the combination of biological, chemical, and physical processes that change the quantity and composition of the organic matter in the upper several hundred meters of marine sediments.” In particular, phytodetritus is vulnerable to diagenetic processes at and near the sediment/water interface (Henrichs 1992, Conte et al. 1995). In oxic bottom waters, phytodetritus is somewhat protected from decomposition and consumption when mixed into the sediment, limiting aerobic remineralisation and diluting the food source for consumers. In hyp- or anoxic bottom waters, consumers are limited in abundance (or absent), and remineralisation is restricted to anaerobic processes (although increasing evidence suggests that anaerobic processes are as efﬁcient as aerobic remineralisation of organic carbon; e.g. Henrichs 1992, Thunell et al. 2000, but see Canﬁeld 1994). In certain cases, even in oxic bottom waters, the accumulation of phytodetritus on the sea ﬂoor “may have overwhelmed” the benthic fauna, creating a physical barrier to mixing that was preserved as a lamination through subsequent sedimentation (Kemp & Baldauf 1993). As described below, phytodetritus may be incorporated initially into underlying sediment through diffusive or advective mixing, and sometimes through burial after little to no mixing. The discussion below focuses on processes occurring on short timescales of days to a year, linking sediment trap ﬂuxes (mg m−2 day−1) to sedimentation rates (cm yr−1). In both oxic and anoxic bottom waters the burial efﬁciency of organic carbon is positively correlated with sedimentation rate (e.g. Henrichs 1992, Thunell et al. 2000). More research is required to bridge the short time-scale sedimentation rates with long time-scale accumulation rates in the fossil layer and geological record (e.g. Carney 1989, C. Smith et al. 1997). Diffusive mixing of the upper sediment, creating the sediment mixed layer, is caused by bioturbation, or the displacement of sediment particles by benthic fauna. In oxic bottom waters phytodetritus is generally incorporated into the sediment through bioturbation (e.g. Graf et al. 1982). Bioturbation may increase in response to the input of phytodetritus, as observed by increased megafaunal activity (“tracking”) at Station M at 4100 m in the northeast Paciﬁc (K. Smith et al. 1994). Alternatively, bioturbation may decrease in response to the accumulation of phytodetritus due to a) satiation of the consumers and reduction of sediment processing, b) oxygen stress from the decomposing organic matter, or c) the “high tensile strength and impenetrability” of diatom mats (described below; Kemp & Baldauf 1993). Diffusive mixing may be modelled as eddy diffusion, assuming a diffusion coefﬁcient (Db) over a ﬁnite depth interval. In food-limited environments subjected to a pulse of phytodetritus, benthic fauna may respond to the input with selective mixing (e.g. Carney 1989), resulting in higher Db for labile organic matter than for refractory organic matter or inert tracers (Blair et al. 1996). In the deep sea, diffusive mixing rates are commonly tracer dependent, with shorter-lived radionuclides such as 234Th yielding higher Db (C. Smith et al. 1993). Such age-dependent mixing, deﬁned as the “process in which recently sedimented, food-rich particles are ingested and mixed at higher rates by deposit feeders than are older, food-poor particles,” has been suggested for several sites in the deep sea (reviewed by C. Smith et al. 1993). In particular, a number of locations at which phytodetritus either accumulated at the sea ﬂoor (abyssal equatorial Paciﬁc sites) or was processed selectively by megafauna (Santa Catalina Basin site) had tracer-dependent mixing, with Db values for 234Th 218

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much higher than for 210Pb. At the abyssal equatorial Paciﬁc sites, Db values for 234Th ranged from 0.36–12 cm2 yr−1 (mean 5.5; C. Smith et al. 1997). At 1240 m in the Santa Catalina Basin, Db values for 234Th ranged from 7.9–200 cm2 yr−1 (mean 60; C. Smith et al. 1993). Future studies of the incorporation of phytodetritus into underlying sediment could include the calculation of Db for chl a, assuming a ﬁrst-order decay constant as in a recent study in the North Sea by Boon & Duineveld (1998). Advective mixing, in which particles are transported between non-adjacent sediment layers, may be due to non-local transport by animals or by deposition into relict burrows and depressions on the sea ﬂoor. On the timescale of hours to days, such non-diffusive transport dominated over diffusive mixing processes in an experiment in the Santa Catalina Basin in which radioisotope-tagged diatoms were deposited on the sea ﬂoor (Fornes et al. 2001). Subsurface concentration maxima of biomarkers in sediment cores provided evidence that mixing of labile organic matter can be controlled by advective rather than diffusive processes in the abyssal northeast Atlantic (Conte et al. 1995). Non-local transport by benthic fauna occurs as a result of feeding activities such as “hoeing” (Levin et al. 1997) and caching in burrows (Jumars et al. 1990). In situ tracer experiments on the North Carolina slope (described above) demonstrated the transport of labelled diatoms to at least 4–5 cm in the sediment in just 1.5 days due to “hoeing” by polychaetes (Blair et al. 1996, Levin et al. 1997). Non-local transport of phytodetritus to 26 cm in the sediment was observed in burrows from abyssal equatorial Paciﬁc sites (C. Smith et al. 1997), and at 1100 m in the Rockall Trough, subduction of phytodetritus to 10–17 cm occurred in relict and active burrows (Black 2001). Localised focusing of phytodetritus into relict burrows was also observed in the abyssal northwest Atlantic (Aller & Aller 1986). Subduction to such depths in the sediment, especially if the burrow is not well-ﬂushed, can lead to anaerobic decomposition of phytodetritus. Burial of phytodetritus in the sediment after little to no mixing is likely in anoxic bottom waters due to the lack of bioturbation by benthic fauna. This lack of mixing can lead to varved sediments, such as the laminated sediments beneath the “permanent” ﬂuff layer in the Black Sea (Pilskaln & Pike 2001). However, anoxic conditions do not necessarily lead to more storage of organic carbon in these laminations (e.g. Thunell et al. 2000). For example, in a recent study of Cariaco Basin, an anoxic silled basin just north of Venezuela with maximum depth ∼1400 m, burial efﬁciency in the upper 10 cm of sediment was 40–60%, admittedly high but within the range of values reported for other anoxic and oxic sites (Thunell et al. 2000). A ﬂuff layer has not been reported for the Cariaco Basin but might be expected due to vertical POC ﬂuxes, deriving mainly from diatoms, up to 32 mg C m−2 day−1 (Thunell et al. 2000). Although not observed in modern times, geological studies of sediment cores from the eastern Equatorial Paciﬁc revealed near-monospeciﬁc laminae of diatoms that were created in oxic bottom waters (Kemp & Baldauf 1993), indicating a major “short circuit” in the recycling of phytodetritus at the sea ﬂoor. For these phytodetrital deposits to be preserved without mixing, Kemp & Baldauf (1993) supposed that the intertwined frustules of the large diatom Thalassiothrix longissima physically prevented bioturbation by benthic fauna. These diatom mats were recorded as sub-millimetre laminations, occurring on the order of annual frequency for restricted time periods between 15 and 4.4 million yr ago. These deposits were also correlated in cores taken over 2000 km apart, suggesting phytodetrital coverage of the sea ﬂoor on a spatial scale more massive than any event documented on Table 1. 219

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Recommendations for further study This review attempted to be comprehensive, especially for the deep ocean, in documenting all observations of the accumulation of phytodetritus on the sea ﬂoor. The search resulted in 10 sites in shallow water and 51 sites in deep water. Although a large amount of quantitative data was summarised for the microscopic and chemical composition of phytodetritus sampled from the sea ﬂoor, limited information exists to determine the quantitative signiﬁcance of these episodic or seasonal inputs to benthic communities and to carbon cycling in general. An ultimate goal of studies of export production and the ﬂux of phytodetritus to the sea ﬂoor is to predict the effects of climate change on the “biological pump” and the resulting effects on storage of organic carbon in marine sediment. The Joint Global Ocean Flux Study (JGOFS) of the 1990s was key to understanding ﬂuctuations in the biological pump in many areas of the ocean but not for the expression of the vertical ﬂuxes on the sea ﬂoor. Understanding where, when, and why phytodetritus accumulates on the sea ﬂoor and the ultimate fate of this material may help decode parts of the geological record and predict future carbon budgets in some areas of the ocean. Several years ago, C. Smith (1994) recognised the need for more quantitative studies of phytodetritus on the sea ﬂoor, suggesting that future research focus on both time-series observations and process studies. In particular, time-series photographic and coring studies should be done in a wide range of locations, including the most oligotrophic to the most eutrophic regimes, to document the magnitude of phytodetrital pulses. In regions where phytodetritus accumulates on the sea ﬂoor, process studies should also determine the energetic signiﬁcance for the benthic community as well as the burial efﬁciency of organic and inorganic components. Today, several long time-series studies in the deep sea include benthic observations and sampling of phytodetritus, as well as targeted process measurements. Four of these studies have lasted on the order of 10 yr; at the BIOTRANS site and Porcupine Abyssal Plain site in the northeast Atlantic, and at Station M and Sagami Bay in the Paciﬁc. Studies at the two sites in the northeast Atlantic involve multiple investigators, who established a precedent with the Atlantic Data Base for Exchange Processes at the Deep-Sea Floor (http:// www.pangaea.de/Projects/ADEPD/). At the Porcupine Abyssal Plain site, speciﬁc process studies were conducted with benthic landers during the recent BENthic biology and Geochemistry of a northeastern Atlantic abyssal Locality (BENGAL) project in 1996–98. At Station M, studies of the initial and longer-term impact of phytodetrital aggregates on SCOC were done during a series of submersible dives in 1994–95. Speciﬁc process studies at Sagami Bay in Japan were done during “Project Sagami” in 1996–99, an intensive effort to understand carbon cycling through the water column to the sediment at this eutrophic site. In addition to these long-term studies, several recent studies of benthic–pelagic coupling at deep-sea sites have been done on the order of 2 yr, such as the deep ocean BENthic BOundary layer (BENBO) project in the Rockall Trough. Time-lapse photographs of the sea ﬂoor and studies of the reproductive response of benthic fauna to the seasonal deposition of phytodetritus are part of the current 2-yr study on the shelf of the Antarctic Peninsula (C. Smith, pers. comm.). A new time-series study, to be launched in 2003 in the central north Paciﬁc, will examine the response of deep-sea, epibenthic megafauna to phytodetrital input in the oligotrophic ocean. This study will utilise the Hawaii-2 Observatory, a cabled sea ﬂoor observatory with real-time data transmitted to shore. In the future more long time-series studies of the accumulation and fate of phytodetritus on the sea ﬂoor should be done from sea-ﬂoor observatories. A sea-ﬂoor observatory is an 220

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“unmanned system of instruments, sensors, and command modules connected either acoustically or via a sea ﬂoor junction box to a surface buoy or a ﬁber optic cable to land” (Ocean Studies Board 2000). Sea-ﬂoor observatories enable monitoring and process studies of the sea ﬂoor without the power and data storage limitations of autonomous landers. To yield critical quantitative information about phytodetrital pulses, sea-ﬂoor observatories must be part of a co-ordinated effort to monitor primary productivity in the upper water column and ﬂuxes through the water column above the sea-ﬂoor site. Benthic observations with the same temporal resolution as surface and water column measurements will allow cross correlation analyses for the time lag between a surface bloom, sedimentation of the bloom, accumulation on the sea ﬂoor and responses by the benthic community. As demonstrated by the recent “Project Sagami”, time-series studies of the biological pump can now span the air/sea interface, the entire water column, and the sediment/water interface. Sea-ﬂoor observatories will also require the development of autonomous instruments to respond quickly to ephemeral events such as the mass sinking of phytodetritus and the accumulation of a ﬂuff layer. As an example, consider the following scenario: suppose that sediment traps moored above the sea-ﬂoor observatory signalled a considerable increase in the downward ﬂux of phytopigments. The command module at the observatory node would then release an autonomous vehicle (AUV) equipped with a scanner such as a spectral imaging system to determine the distribution of phytopigments on a swath of the sea ﬂoor. After completing the transect, this AUV would return to the node, dock, and then wait a speciﬁed time before going out again to scan the sediment surface for fresh phytodetritus. At the same time, the command module would trigger the release of benthic chambers for SCOC and other ﬂux measurements. The temporal resolution of time-series, pore-water proﬁlers (already operating near the node) might be increased, with measurements of oxygen, dissolved organic carbon, or redox-sensitive metals taken twice a day. The command module would also initiate a time series of core samples, with in situ preservation for counts of bacteria, meio- and macrofauna upon recovery at a later date. With such measurements at a variety of sites in the global ocean, we are sure to improve our quantitative understanding of the processing of phytodetritus on the sea ﬂoor.

Acknowledgements My inspiration for writing this review was kindled by my ﬁrst dive in Alvin and stimulated by discussions with K. Smith and R. Lampitt. I give special thanks to the librarians at the Marine Biological Laboratory. I also thank L. Lauerman, D. McGillicuddy, J. Morford, C. Smith, and K. Smith for reviewing sections of the paper. I thank many people who provided data and/or reviewed the tables: D. Billett, K. Black, M. Conte, G. DiTullio, E. Escobar-Briones, S. Goffredi, C. Grenz, J. Gutt, L. Keigwin, H. Kitazato, K. Kormas, L. Lauerman, T. Leipe, L. Levin, P. Linke, O. Pfannkuche, C. Riaux-Gobin, E. Sauter, C. Smith, K. Smith, L. Thomsen. I thank J. Quinlan for help with Figure 1. I received support for writing this review from the Exxon Foundation through a Woods Hole Oceanographic Institution Postdoctoral Scholarship, from NSF grant #OCE0002223 to J. Trowbridge and S. Beaulieu and from NSF grant #OCE9725974 to D. McGillicuddy. This is WHOI contribution #10529. 221

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IMPACT OF CHANGES IN FLOW OF FRESHWATER ON ESTUARINE AND OPEN COASTAL HABITATS AND THE ASSOCIATED ORGANISMS 1

BRONWYN M. GILLANDERS1 & MICHAEL J. KINGSFORD2 Department of Environmental Biology, University of Adelaide, SA 5005, Australia Ph (61 8) 8303 6235; Fax (61 8) 8303 4364; e-mail: [email protected] 2 School of Marine Biology and Aquaculture, James Cook University of North Queensland, Townsville, QLD 4811, Australia

Abstract Freshwater is scarce in many regions of the world. In some areas, water entitlements currently exceed the available water supply yet few proposals for regulating quantities extracted are scrutinised in terms of possible impacts or undergo any form of rigorous impact assessment. In addition, there is little understanding of the potential impacts. There is a growing need to understand better the impact of altered ﬂows of fresh water on estuarine and open coastal marine systems. There is a perception that fresh water is lost when it enters the marine environment. We argue that freshwater–saltwater dynamics have profound inﬂuences on coastal ecosystems. The purpose of this paper is to review the nature of freshwater discharges and the effects of fresh water on the physical aspects of estuaries as well as estuarine and marine ﬂora, fauna and habitats. Although the review focuses on decreased ﬂows to marine systems, major increases in ﬂow can also have a major impact on estuarine and coastal systems. Freshwater runoff is a function of numerous environmental variables, depending primarily on climate (precipitation and evaporation) and the physical characteristics of the drainage basin. Anthropogenic activities in catchments may result in diversions and reductions in freshwater ﬂow, alterations of timing and rates of ﬂow to estuarine and coastal systems, and/or adverse water quality conditions with major changes in nutrient loading. Sediment loads, pH, temperature, salinity, clarity, oceanography and nutrients are affected. Perturbations in coastal systems can be freshwater pulses (i.e. storms or opening of ﬂoodgates) or press scenarios (i.e. persistent ﬂow of low variation from rivers or industry). Impacts on organisms can also be categorised as pulse events (where there is a rapid but not sustained change), or press events (where changes are sustained over long periods of time). Changes to freshwater input affect habitats and organisms within estuaries. The effects include mortality, changes in growth and development, and in some cases movement of organisms. Major mortalities are most likely during pulse events of freshwater input. There is considerable descriptive and small-scale experimental evidence to suggest that a variety of organisms may be affected by changes to freshwater input. Much of the experimental evidence focuses on single factor experiments and rarely have there been multifactorial experiments (an exception is seagrasses). In addition, there have been no large-scale experiments (e.g. size of sample unit 10’s to 100’s of metres), although it is acknowledged that such experiments will be difﬁcult. We suggest that any changes in water management (e.g. removal of water for irrigation) should be treated as manipulative

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experiments and that estuarine and marine systems are monitored together with reference or control locations (where there has been no change) to determine the impact of such changes. At the large scale, ﬁnding suitable control locations could be difﬁcult. Data should be collected multiple times prior to and after the change has occurred. In the ﬁrst instance, objective scientiﬁc evidence should be used for decision-making and when that is not available, we suggest that the principle of precautionary action should be adhered to. In conclusion, freshwater ﬂows have a great impact on physical and biological aspects of coastal environments. The impacts of fresh water on marine environments, as well as terrestrial environments, should therefore, be considered by managers.

Introduction Freshwater is now scarce in many areas of the world; it constitutes ∼2.5% of the total volume of water on earth, and two-thirds of this is locked in glaciers and ice caps (Postel et al. 1996). The major demand for freshwater is from agriculture, although other uses include industries, such as electricity generation, and urban uses (Postel et al. 1996). Worldwide, irrigation accounts for two-thirds of water extractions (Postel 2000). Although irrigation is the major consumer of the world’s available freshwater, the efﬁciency of irrigation schemes is only about 30% (Petts 1990), suggesting that large amounts of freshwater may be wasted. Regulation of rivers and diverting water for irrigation is affecting the hydrology of rivers and wetland ecosystems (Walker 1985, Kingsford & Thomas 1995). In Australia, for example, few proposals for regulating rivers are scrutinised in terms of possible impacts or undergo any form of rigorous impact assessment. Freshwater discharge into estuarine and marine environments varies on predictable timescales showing diel, seasonal and/or annual patterns, although stochastic variability is also common. Man-made alterations to patterns of freshwater discharge change natural stream ﬂow and potentially affect physical aspects (water chemistry and sediments), habitats and their marine ﬂora and fauna. The effects of freshwater ﬂow are important to managers of the marine environment. Reduced freshwater inﬂow can, for example, increase salinity of the water column, allowing marine ﬂora and fauna to colonise upstream, replacing brackish water communities (Wortmann et al. 1997). Alternatively, release of freshwater into estuarine habitats by opening ﬂoodgates can drastically change salinity from full sea water (salinity 35) to full freshwater and back to full sea water again over short intervals of time (Irlandi et al. 1997). Besides changing salinity, alteration of freshwater volume and ﬂows into coastal marine waters may change the temperature and nutrient regimes, alter the extent of estuarine plumes (Grimes & Kingsford 1996), reduce the extent of wetlands and degrade estuarine and nearshore habitats (Serafy et al. 1997), and remove cues for migration. The demand for water is increasing, seriously threatening the sustainability of ecosystems (Postel 2000). Different practices of water management potentially inﬂuence many types of habitats and organisms, and may have ﬂow-on effects to ecosystem functioning. The consequences have only recently been considered. Large-scale anthropogenic modiﬁcations of freshwater inﬂow to estuaries are threatening the existence of estuarine habitats (Schlacher & Wooldridge 1996). Within South Africa, freshwater abstraction varies greatly from estuary to estuary, and in some catchments the storage capacity of reservoirs exceeds the annual runoff from rivers feeding them (Schlacher 234

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& Wooldridge 1996). Likewise, in some regions of New South Wales (Australia), water entitlements on irrigation licenses exceed the available water supply (Kingsford 1999). It has been suggested that “over exploitation of water is fast becoming one of the worse environmental legacies for future generations” (Kingsford 1999). Worldwide, there are many examples of the serious ecological impacts of water diversion (e.g. Egypt: Aleem 1972; Aral Sea: Micklin 1988; California: Wiens et al. 1993; Turkey: Green et al. 1996; see also Lemly et al. 2000 and case studies). The quantity and timing of ﬂow for downstream areas such as wetlands, lakes, rivers and estuaries is not known. Although minimum ecological ﬂows downstream of dams or abstraction points have been set in some cases, the level is often arbitrary with little ecological signiﬁcance (Boon 1992). The ecological needs of downstream estuaries and coastal zones have rarely been considered in allocating ﬂows in Australia (Zann 1996), although this is starting to change. Loneragan & Bunn (1999) suggest that there is still a strong perception that “water going to sea is wasted”. Downstream areas are often out of the jurisdiction of the agency responsible for the upstream water development project, there is a lack of interest in following up major projects after they have been completed, and impact assessment of downstream areas is highly complex and expensive (Rosenberg et al. 1995). In addition, most water users only look upstream at practices impacting their access to water or its quality, and rarely do they look downstream. In general, the inﬂuence of freshwater input on estuarine and marine assemblages is poorly documented. The boundaries of marine ecosystems are not easily deﬁned because physical conditions and movements of organisms (i.e. pelagic life history stage followed by post-settlement movement to other habitats), as well as their prey, ﬂuctuate over large distances in the sea and this ﬂuctuation should be kept in mind when dealing with ecological processes in oceans inﬂuenced by freshwater outﬂow (Skreslet 1986a). The general relationship between catchments, surface waters, ground waters and the coast is shown in Figure 1. Inland waters originate in catchments and water moves across the surface of catchments or through the soil in groundwater. Any changes to part of the catchment can thus cause downstream effects including in estuaries and coastal waters. There are, therefore, many interdependent links

Figure 1 General relationships between catchments, surface waters, groundwaters and the coast. From Wasson et al. (1996), Commonwealth of Australia copyright reproduced by permission.

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among land use, inland waters, and estuaries and coastal waters. Altering the source, timing and velocity of freshwater ﬂows inﬂuences the salinity and temperature patterns, sediments, nutrients, dissolved oxygen and concentrations of pollutants within estuaries and coastal waters. The purpose of this paper is to review the effects of freshwater discharge and its variations on estuarine and marine ﬂora, fauna and habitats with some mention of freshwater habitats, particularly where a species is found in both fresh water and marine habitats (e.g. migrating ﬁshes or birds). There is a strong emphasis on Australian systems because the hydrological conditions in Australia differ a great deal from the rest of the world, with the exception of southern Africa (Karim et al. 1995, Puckridge et al. 1998, see also Nature of freshwater ﬂows, p. 237). There are reviews of the inﬂuence of water regulation on freshwater environments (e.g. Walker 1985, Kingsford 2000) and methods for estimating instream ﬂow requirements (i.e. ﬂows that are essential within a stream to maintain its natural resources at some speciﬁed or desired level, Arthington & Pusey 1993, Karim et al. 1995). There is little evidence that ﬂows to estuaries have actually been explicitly considered in these reviews or methods (but see Powell & Matsumoto 1994, Jassby et al. 1995). For the Northern Hemisphere, there is also some discussion of the role of freshwater outﬂow on coastal marine ecosystems and ﬂuxes in estuaries (Skreslet 1986b, Dyer & Orth 1994). There are some reviews that deal with aspects of the impact of freshwater on marine systems but they generally refer to speciﬁc subject areas (e.g. the inﬂuence of freshwater outﬂow on recruitment of marine organisms, Grimes & Kingsford 1996). This review aims to synthesise major ﬁndings, point out the weaknesses of some paradigms and highlight areas for future research. The importance of freshwater for estuarine and marine processes and systems, as well as the inﬂuence of natural and anthropogenic factors are described. Habitat-forming species include salt marsh, mangroves, seagrasses, macroalgae and coral reefs. Finally, the effects of freshwater on other ﬂora and fauna are reviewed. The primary interest is in papers that describe patterns of ﬂow in rivers and environmental variables (e.g. salinity), strongly inﬂuenced by freshwater ﬂows, as well as papers that examined the inﬂuence of freshwater on estuarine and marine assemblages or habitats. Unpublished papers and those in the “grey” literature are seldom cited because they are generally not available but investigators are urged to seek local information where possible.

A framework for the review Methods were evaluated to determine whether they were appropriate or inappropriate and whether they were correctly or incorrectly applied. It was important to distinguish pattern from processes. A process is the cause of the observed pattern. Most ecological research begins with the description of patterns or observations of some kind of relationship (Andrew & Mapstone 1987, Underwood 1990, 1997). The most comprehensive descriptions are at a range of spatial and temporal scales. This range of scales is particularly relevant to riverine systems that have great spatial and temporal variation in perturbations (e.g. ﬂoods), and responses of organisms and abiotic characteristics. Potential explanations of the proposed patterns can then be made, and hypotheses generated. The hypotheses may concern the patterns themselves or the processes that govern them (Andrew & Mapstone 1987). Experiments can then be used to test hypotheses about processes. 236

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Many studies on effects of freshwater input measure the abundance, biomass or diversity of organisms. For example, the production of shrimps varies in an estuary over many years. One explanation for the variation is that production of shrimps responds to changes in freshwater discharge. The hypothesis is typically tested by correlating production of shrimps with freshwater discharge, using data that have been collected over many years. By correlating production with freshwater discharge an association between the two variables can be found, but association does not necessarily imply causation. Correlative approaches are common in the literature on freshwater ﬂows to marine systems. This approach is taken partly because observations cover whole estuaries but also because the organisms are frequently motile and move over large distances. Many studies perform a number of correlations, thereby increasing the chance of incorrectly ﬁnding a signiﬁcant correlation (Type I error, Underwood 1997). In addition, in the case of large datasets signiﬁcant correlations may have little or no biological relevance and there may be correlation among variables. For example, freshwater discharge is often inversely related to salinity and temperature. Correlative approaches may suggest useful predictive hypotheses but manipulative experiments are needed to attribute causality. Such experiments may be difﬁcult at spatial scales relevant to estuaries and can also be prevented by competing demands of users. Natural or mensurative experiments (sensu Hurlbert 1984) on freshwater input to marine systems are common in the literature, mainly because of the difﬁculties of manipulating whole estuaries (see also Underwood 1990). Instead of creating the conditions required by the prediction, the researcher ﬁnds places that have those conditions naturally. For example, instead of increasing salinity within estuaries, a researcher could ﬁnd estuaries that had increased levels of salinity. Ideally, these would be compared with estuaries that have no change in salinity, but this approach is rarely practical. Conclusions may be confounded because the estuaries are not the same and comparisons of multiple estuaries have to be made over large spatial scales. Research papers were evaluated using the following criteria: a) whether the research was relevant to the impact of fresh water on estuarine and marine systems; b) if the design was confounded; c) if the analysis and interpretation of the data were correct and the conclusions valid. It was sometimes difﬁcult to evaluate these criteria because there was insufﬁcient information. Studies that did not meet these criteria but provided useful information to generate hypotheses were also considered.

Nature of freshwater ﬂows Freshwater enters estuarine and ocean waters from many sources including: rivers, agricultural runoff, industrial and sewage outfalls and urban stormwater. Water quality gradients may be related to spatial proximity to nutrient point sources (e.g. sewage outfalls). River waters often carry large amounts of particulate material in the form of clay particles and organic detritus that generate a shallow turbid layer (Grimes & Kingsford 1996). Dissolved organic material, nutrients and pollutants are also transported in river waters. For example, high concentrations of nitrate, phosphate and chlorophyll a are often associated with the entrance of rivers into estuaries (e.g. Boynton et al. 1996). There may be spatial differences in patterns of turbidity and water quality related to location within estuaries. 237

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Natural factors inﬂuencing freshwater input Freshwater runoff depends primarily on climate and the physical characteristics of the drainage basin (see also Fig. 1). Controls on climate such as precipitation and evaporation that govern runoff will have a signiﬁcant impact on freshwater input to estuarine environments. Patterns of river ﬂow have been described in terms of mean monthly ﬂow (Fig. 2 and

Figure 2 The distribution of patterns of mean monthly ﬂow and seasonal river regimes in Australia. Mean monthly ﬂow is expressed as a percentage of mean annual ﬂow for stations in each group and the ﬁrst month of summer is indicated on the x-axis as 1. Axis labels are only indicated on moderate early summer. Mid summer, extreme late summer and moderate spring ﬂows occur in small areas and are not shown on the map of Australia. Adapted from Finlayson & McMahon (1988), published with permission.

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Figure 3 Rainfall in the Sydney metropolitan region showing actual monthly rainfall and mean monthly rainfall (averaged over a 30-yr period). Although pulses of rainfall appear to occur over a month-long interval, many actually occur over a few days within each month. Data were extracted from the Bureau of Meteorology Monthly Rainfall Reviews.

Finlayson & McMahon 1988), although there can be marked differences from levels of mean ﬂow (Fig. 3). Seasonal variation in river ﬂow is also marked and can be described in terms of latitude (Fig. 2: Walker 1985, Finlayson & McMahon 1988, Kench 1999). Rivers at high latitudes have a sustained discharge but seasonal peaks typically follow melting of snow or glaciers as well as rainfall (Walker 1985). Although snowmelt in the mountains may lead to maximum water levels in spring or early summer in some areas of the world (e.g. Sweden, Jansson et al. 2000), snowmelt is not an important factor inﬂuencing ﬂow in most Australian rivers (Finlayson & McMahon 1988). Flows of rivers at mid latitudes (e.g. Australia) tend to be less predictable (Walker 1985, Puckridge et al. 1998). Australian rivers typically exhibit high annual variability in discharge, across a range of sizes of catchments. Although there is little information on the frequency of ﬂood events, ﬂoods range from annual events by monsoon rains to virtually nothing (Bucher & Saenger 1991). Differences in peak ﬂood events relative to mean annual rainfall are an order of magnitude greater in Australia compared with the rest of the world (except southern Africa, Finlayson & McMahon 1988). Australian and southern African rivers exhibit more extreme ﬂood behaviour than those found on other continents (Finlayson & McMahon 1988, Puckridge et al. 1998). Although many researchers have generalised patterns of ﬂow throughout the world including Australia, there is also a stochastic nature to river ﬂows resulting from pulse rainfall events (see for example, Fig. 3 and Benke et al. 2000). Thus, it is likely to be difﬁcult to make accurate predictions of actual river ﬂow from knowledge of mean monthly levels, particularly in southern regions of Australia and Africa. In the Sydney metropolitan region (NSW, Australia), stochastic events meant that actual monthly rainfall was sometimes 3– 4 times that expected on average (Fig. 3). Over a 10-yr period, actual monthly rainfall was 239

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greater than that predicted by the mean values for 33% of the time. Rainfall within each month often occurred over just 2–3 days. Global-scale atmospheric circulation anomalies have caused unusual precipitation in many areas of the world (Molles & Dahm 1990). Climate and variability in stream ﬂow have been related to El Niño and La Niña Southern Oscillation events. Spring ﬂows in two New Mexico (USA) rivers increased signiﬁcantly during El Niño years when sea surface temperatures were elevated and the barometric pressure was reduced in the eastern Paciﬁc (Molles & Dahm 1990). Increased ﬂows result in elevated river discharge to coastal systems. Signiﬁcantly decreased ﬂows occurred during La Niña years. Likewise, during La Niña years, coastal water levels and inundation of wetlands were anomalously low due to climatological events related to El Niño Southern Oscillation (ENSO) events (Childers et al. 1990). During El Niño/La Niña cycles the reverse patterns to that seen in many areas of the world are found within Australia (Allan et al. 1996). Physical characteristics of the drainage basin may also inﬂuence river ﬂow. For example, in most coastal regions of Australia, catchments are small and of low relief, and the rivers generally short, which results in small amounts of river discharge. Once fresh water discharges into estuarine regions, its inﬂuence within estuaries and on offshore waters will depend on tidal regimes, wind, topography of the entrance of the estuary and the nature of ocean currents. The size and shape of low-salinity plumes, and associated fronts will also be inﬂuenced by the direction and intensity of ocean currents (e.g. Kingsford & Suthers 1994, see also p. 246). Many of the factors inﬂuenced by natural processes are also inﬂuenced by anthropogenic factors that inﬂuence ﬂow of freshwater.

Anthropogenic factors inﬂuencing ﬂow of freshwater Water ﬂow People have used rivers for ﬁshing, boating, discharging waste, and abstraction, among other things (e.g. bathing, washing). The headwaters of rivers have been diverted, their middle reaches have been dammed and their ﬂoodplains developed (Boon 1992). All these changes affect freshwater input to estuaries. Boon (1992) summarised the range of human activities that were potentially damaging to river systems into four categories, effects above or beyond the level of catchments, land-use changes within catchments, corridor engineering and instream impacts. A number of anthropogenic activities within each of these four categories were recognised (Table 1). Inter-river transfer schemes (i.e. transfer of water from one river basin to another) are being developed as suitable sites for dams become scarcer (Boon 1992). One of the largest schemes proposed involves transferring 60 km3 of water annually from the River Ob in Soviet Central Asia (Petts 1990). This scheme involves transferring water 2200 km to support irrigation and to counteract problems created by water abstraction from other rivers (Petts 1990). The impacts of such a large-scale inter-basin transfer are potentially wide ranging including reducing thermal inputs to the Ob Gulf, increasing ice cover, delaying spring melt of ice and causing saline intrusion (Petts 1990). Flora and fauna, as well as diseases and parasites, are also likely to be transferred across rivers. Effects within catchments such as clearing of land may result in local rises in the water table, as well as increased surface runoff (Davis & Froend 1999). 240

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Table 1

Major anthropogenic activities affecting river systems (modiﬁed from Boon 1992).

Effects above or beyond the catchment Acid deposition Inter-basin transfers Catchment land-use change Afforestation and deforestation Urbanisation Agricultural development Land drainage/ﬂood protection Corridor engineering Removal of riparian vegetation Flow regulation – dams, channelisation, weirs, etc Dredging and mining Instream impacts Organic and inorganic pollution Thermal pollution Abstraction Navigation (e.g. wake effects) Exploitation of native species Introduction of exotic species

The natural ﬂow of river systems and the frequency, extent and duration of ﬂoods may be altered by the construction of dams, weirs and levee banks. It has been estimated that by the year 2000, two-thirds of the world’s stream ﬂow will be regulated to supply downstream users through dams and weirs (Boon 1992). Large dams, in particular, have the ability to capture large volumes of water to the extent that small and medium sized ﬂoods can be entirely absorbed into storage, but this effect will be dependent on the level of water already in the reservoir. The dramatic effects of dam construction on water ﬂow can be seen for the Colorado River at the USA–Mexico border (Fig. 4 and Lavin & Sanchez 1999). Prior to 1935 (when the ﬁrst major dam was constructed) large amounts of fresh water were discharged into the Gulf of California and showed a seasonal cycle (Fig. 4). The river now no longer discharges regularly into the Gulf of California and the seasonal cycle has been damped because freshwater is retained in dams and diverted for urban consumption and irrigation (Lavin & Sanchez 1999, see also case study on Colorado River, p. 245). Early records of ﬂow in the River Murray (recorded at Blanchetown, South Australia, Australia) also show ﬂuctuating water levels, but after regulation in 1922 the pattern of high and low ﬂows changed, and river levels were relatively constant (Walker 1985, Maheshwari et al. 1995). Stream ﬂow can even cease during dam construction. For example, ﬂow was terminated on at least three occasions during construction of the Tallowa dam on the Shoalhaven River (NSW, Australia), so that within 40 min much of the river was reduced to a dry bed leaving ﬁsh stranded and dying (Bishop & Bell 1978). Seasonality of ﬂows may be affected by regulation. In the River Murray a strong seasonal pattern is still evident but winter and spring ﬂows are captured into storages and released during summer for irrigation (Cadwallader & Lawrence 1990). This seasonal shift is dissipated with increasing distance downstream. Likewise, discharge has been displaced from spring to winter months in the St Lawrence 241

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Figure 4 Flux of water of the Colorado River across the US–Mexico border. (a) Filling of Hoover Dam (Lake Mead) February 1935–July 1941, (b) ﬁlling of Glen Canyon Dam (Lake Powell) March 1963–June 1980, (c) excess releases May 1979–January 1981, (d) abnormal snowmelts in the upper basin, and (e) end of excess releases June 1987. From Lavin & Sanchez (1999), published with permission.

system in North America (Sinclair et al. 1986). Dams and weirs generally result in higher average water levels in freshwater reaches above the structure. Fluctuations in water levels within regulated rivers can also be more rapid than would have occurred naturally. Regulation of rivers also impacts temperature and salinity regimes, altering water quality and affecting the location of the tidal prism. Regulation of rivers lowers downstream temperatures (and sometimes dissolved oxygen) especially where there is deep-release from reservoirs, with effects up to 200-km downstream (Cadwallader & Lawrence 1990). Because dams and other barriers suppress downstream ﬂooding, there may also be an increase in salinity. Barrages located near the mouth of rivers can restrict the tidal prism, an important factor inﬂuencing river mouths during periods of no river ﬂow (Bourman & Barnett 1995). The mouth of the Murray River has been closed for weeks during some years. River regulation may affect sedimentation rates in coastal areas by changing sediment loads, settling velocities and bottom scouring. Sediment that is normally transported to the coast by rivers may be deposited in upstream reaches behind weirs or within the basins of lakes (Bourman & Barnett 1995, Stanley & Warne 1998). A reduction in ﬂuvial sand input retards the natural transition of estuarine systems from estuarine tidal to ﬂuvial dominance (Roy 1984), sometimes reducing or removing estuarine environments. For example, the Burnett River (Queensland, Australia) has had its estuary reduced by 40% due to the construction of a tidal barrage (QDPI unpubl. report).

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When river runoff is reduced, pollution problems may develop or worsen, because less water is available for dilution. In addition, contaminants may have longer residence times and show more rapid deposition at the bottom of estuaries and in near-shore coastal regions (Drinkwater & Frank 1994). Contaminants are then likely to have negative impacts on benthic organisms.

Contaminants In many regions of the world, rivers are used for waste disposal, again altering the natural ﬂow of the river system. Discharge of wastewater typically leads to increases in nutrients to groundwater, streams and coastal waters (Valiela et al. 1992). Wastewater may arise from industries such as tanneries, plastic, pesticide and chemical manufacturers, textile, dye and cement factories, as well as mining operations (e.g. gold mines). Waste from open or broken sewer lines and untreated or poorly treated sewage from homes is often dumped directly into streams, increasing after storms (Glasgow & Burkholder 2000). Agricultural land use and deforestation increases loads and changes ratios of nutrients to waterways, groundwater and coastal bays. For example, agriculture was responsible for 65– 83% of total nitrogen and 10–36% of total phosphorus transport each year from rivers to Danish coastal waters (Kronvang et al. 1995). Furthermore, median concentrations of total nitrogen and phosphorus were higher in rivers draining agricultural catchments than in rivers draining undisturbed catchments (Kronvang et al. 1995). This effect varied with soil characteristics so that there was a greater percentage of nitrogen from surface waters in loamy soil than in sandy soil catchments (Kronvang et al. 1995). In Western Australia, forested catchments effectively trapped nutrients, such that nitrogen and phosphorus concentrations leaving catchments were relatively low. Mean nitrogen concentration in stream ﬂow was linearly related to percentage of catchment cleared (McComb & Lukatelich 1995), although there was no indication of the goodness of ﬁt of this relationship. Nutrient enrichment is a major problem in coastal waters but there is little information on basic relationships between rates of nutrient supply and key ecological processes (Valiela et al. 1992). If estuaries are not ﬂushed by sea water (e.g. when the entrances of estuaries are closed by sandbars), problems with nutrient enrichment and associated eutrophication may be enhanced (Potter et al. 1990). Acidiﬁcation occurs naturally but has been exacerbated by extensive engineered drainage of coastal ﬂoodplains (Sammut et al. 1996). Sulphuric acid, generated from oxidation of pyrite reacts with silica and metal ions (e.g. aluminium, iron, potassium, sodium, magnesium) to release ions that can be highly toxic to organisms. Toxicity of different concentrations of trace metals depends on pH, the particular species of ion present and their possible complexation with dissolved ligands (Driscoll et al. 1980, see above).

Nature of impacts It is useful to consider changes in river ﬂow and the responses of organisms as impacts. An impact is an alteration in the ecology or physiology of some members of an assemblage caused by a perturbation or disturbance. The persistence of a perturbation can vary greatly, although two kinds of perturbation are recognised (Bender et al. 1984). In the ﬁrst type, the perturbation is made and then the assemblage responds and returns to pre-impact conditions

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quickly (pulse), whereas in the second type the alteration or potential impact is maintained (press). The pulse perturbation is a relatively instantaneous alteration, after which the system recovers once the potential impact has ceased. The press perturbation, by contrast is a sustained alteration that may lead to complete elimination of some species. Both the nature of the impact and the response of the organisms can be either press or pulse (Glasby & Underwood 1996). Thus, a pulse disturbance (e.g. a ﬂooded river from a few days of heavy rainfall) may show either a short-term (pulse), or a continuous (press) response. A ﬂood (short pulse perturbation), for example, could result in a long-term reduction in abundance of seagrass (press response).

Case studies Four examples follow of highly productive ecosystems suffering when dams and diversions either sever a river’s connection with the sea or the amount of freshwater reaching the sea is reduced to a small proportion of its pre-anthropogenic levels. There are also systems that have experienced natural increases in river ﬂow and discharge. For example, changes in the frequency of the southern oscillation and El Niño/La Niña events have contributed to a decadal level increase in ﬂow of the Mississippi River this century.

Nile River A classic example of anthropogenic impacts on freshwater supply is that of the building of the High Dam of Aswan in Egypt. Construction of this dam, and the subsequent cessation of ﬂoodwater from the Nile discharging into the Mediterranean Sea, has had a major impact on marine life in coastal waters and on brackish water life in the lakes (Aleem 1972, White 1988, Stanley & Warne 1993). The Nile River system is now so modiﬁed that nearly all water is diverted and no fresh water reaches the sea (Stanley & Warne 1993). Floodwaters discharged at rates of 640 million m3 day−1 prior to dam construction in the 1960s. The large decrease in fresh water has resulted in increased salinity in the coastal region (Drinkwater & Frank 1994). The salinity in northern portions of lagoons adjacent to the coast has also increased, although this may be as much a response to sea level rise as water regulation (Stanley & Warne 1998). Marine intrusion of groundwater has accelerated since construction of the High Dam. Prior to emplacement of barrages on the Nile, the sediment load was primarily silt carried in suspension, much of which was transported to and beyond the delta coast (Stanley & Warne 1998). Since construction of the High Dam, <2% of sediment bypasses the High Dam turbines, and <10% of the former Nile River sediment load now reaches the Mediterranean coast (Stanley 1996, Stanley & Warne 1998). Reduced sedimentation has led to increased erosion on the delta coast, with the possibility that the saline lakes will change to coastal bays. Phosphates, silicates and nitrates all increased in concentration shortly after the ﬂoodwaters reached the sea, and resulted in massive blooms of phytoplankton. The zooplankton community also reached a maximum during the period of massive phytoplankton production. With the dam now storing water, there have been massive decreases in mud, silt and nutrients reaching the delta (Aleem 1972). Phytoplankton blooms have failed to develop, sardine (Family Clupeidae) catches have dropped considerably and lake-ﬁsheries have decreased. 244

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Colorado River The lower Colorado River and delta have been affected severely by river impoundment and water diversions to the point where the river no longer discharges regularly into the Gulf of California (Carriquiry & Sanchez 1999, Lavin & Sanchez 1999, Galindo-Bect et al. 2000). Prior to 1935, and between 1941 and 1960, large amounts of freshwater ﬂowed into the Gulf of California varying on a seasonal cycle (Fig. 4 and Lavin & Sanchez 1999). These ﬂows virtually ceased over the next 20 yr while a large dam was ﬁlled. Since 1980, only occasional ﬂood releases have entered the upper Gulf of California. Reductions in river discharge have caused dramatic increases in salinity in the estuary to the point where the upper Gulf of California is now an inverse estuary (Lavin et al. 1998, Lavin & Sanchez 1999). Thus, the salinity increases from its entrance (∼35.4) to the head (39.0 in summer, 37.0 in winter, Lavin & Sanchez 1999). Dam construction and river diversion have effectively trapped most of the sediments entering the Gulf. The large tidal range (12 m) and wave action have further eroded and resuspended bottom sediments in the estuarine basin, causing serious ecological impacts to the fauna and ﬂora (Carriquiry & Sanchez 1999). This region once supported an important commercial and sport ﬁshery for the endemic totoaba (Totoaba macdonaldi, Sciaenidae), which is now endangered (Barrera Guevara 1990). Another endangered species in the region is the vaquita porpoise (Phoceona sinus). Decreases in river discharge to the delta and estuary have also led to a signiﬁcant reduction in shrimp landings (Galindo-Bect et al. 2000) and a dramatic drop in population levels of macrofauna (Kowalewski et al. 2000, Rodriguez et al. 2001). The upper Gulf of California is now designated as a Biosphere Reserve.

Yellow River Regulation and abstraction have reduced ﬂows in the Yellow River in China. The key issues in this river are ﬂood prevention associated with sediment control, as well as utilisation of the river’s water resources and hydropower generation (Changming 1989). Flows have been used to such an extent that since 1972 the Yellow River has run dry in its lower reaches, with the dry section often stretching 600 km. The number of days in which the river has run dry has increased over time. In 1997, it ran dry for 226 days, compared with 133 days in 1996 and 122 days in 1995 (Postel 2000). Floods within the river are characterised by high peak discharge over a few days but also by large amounts of sediment due to severe soil erosion in the catchment. The Yellow River has sediment problems that are unique in the world (Xiutao 1986, Changming 1989, Ren 1994). The sediment content in the river and its tributaries can sometimes exceed 600 kg m−3. The river’s channel in its lower reaches has silted progressively and many sections of the riverbed have risen creating a “suspended river” (Changming 1989). Slow ﬂows, shallow water in estuaries and weak tidal energy means that only a small portion of the sediment can be transported into deeper seas. The capacity of the downstream sections of the river to transport sediment has been reduced by removing water for irrigation and storing water for power generation. The sediment load of the river also has a direct effect on the delta. Between 1976 and 1989, an average of 35 km2 of new land was formed per year (Ren 1994) but more recent estimates suggest that the sandy coast is retreating (Wang 1998). 245

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Ganges River The Ganges is one of the world’s top ten rivers in terms of annual runoff with its basin spreading through China, India, Nepal and Bangladesh. The Ganges exhibits two extreme ﬂow characteristics, namely ﬂoods during the monsoon season and scarcity of water during dry seasons. Scarcity of water during the dry seasons has been exacerbated by India’s decision to build a barrage, on the Ganges just upstream of the Bangladesh border, to divert water into another river (inter-river transfer) to improve the navigability of the port of Calcutta (Khan 1996, Patel 1996). This transfer has resulted in the ﬂow of the Ganges in Bangladesh being reduced to a trickle during the dry season. Tides from the Bay of Bengal have moved further up the river when the ﬂow is reduced such that salt water may penetrate 100 km inland (Allison 1998). The increased salt concentrations are killing trees in the largest mangrove forest in the world and are having a devastating impact on ﬁsheries in the region (Patel 1996).

Effects of altered freshwater ﬂows on physical factors Estuarine and offshore environments Estuarine environments are at the transition between open coast and riverine environments. According to Perillo (1995) an estuary is a semi-enclosed coastal body of water that extends to the effective limit of tidal inﬂuence, within which seawater entering from one or more free connections with the open sea, or any other saline coastal body of water, is signiﬁcantly diluted with freshwater derived from land drainage, and can sustain euryhaline biological species for either part or the whole of their life cycle. The morphology of estuaries varies widely (Fig. 5), and the effects of freshwater will vary depending on the type of estuary (Table 2). Morphology of estuaries varies from drowned river valleys (e.g. Chesapeake Bay and Delaware Bay along the Atlantic coast of USA, Hawkesbury River, Port Hacking and Batemans Bay in New South Wales (NSW), Australia), to lagoon-type blind and bar-built estuaries (e.g. Pamlico-Arbermarle sound in North Carolina, USA, Clarence, Merimbula and Narooma estuaries in NSW, Mallacoota in Victoria, Australia, Great Fish and Kariega estuaries in South Africa) to fjord-type estuaries (e.g. Puget Sound in Washington, USA, Sogne Fjord in Norway, Milford Sound in New Zealand) to tectonically produced estuaries (e.g. San Francisco Bay, USA, Fig. 5). Riverine discharge into coastal lagoons, estuaries and ultimately oceans leads to turbid low-density plumes extending into coastal waters (Grimes & Kingsford 1996). The size of plumes may vary from <10 km long (e.g. Botany Bay, Australia: Kingsford & Suthers 1994) to >100 km long (e.g. Amazon River, Brazil: Curtin 1986a,b) (Table 3). Large rivers typically produce large plumes. The local seasonality of rainfall, as well as snow melt, tidal regime, wind, topography of the coastal environment and the nature of ocean currents outside estuaries may also inﬂuence low-density plumes (Grimes & Kingsford 1996, Ardisson & Bourget 1997). 246

FRESHWA TER EFFECTS ON ESTUARI N E A N D C O A S T A L H A B I T A T S

Figure 5 Morphological classiﬁcation of common estuary conﬁgurations. From Fairbridge (1980), published with permission.

Three types of water have been recognised offshore of plumes of river discharge, namely plume water, shelf or open ocean water and frontal water (Govoni et al. 1989, Kingsford & Suthers 1994, Grimes & Kingsford 1996). The frontal waters are a mixture of plume and shelf waters and are deﬁned by their density or thermohaline signature and distinctly visible turbidity front. Frontal zones may vary over different spatial and temporal scales. Those of rivers with consistently high water volumes are more or less permanent structures and can be seen over spatial scales of kilometres (e.g. Amazon River plume – 25 km wide: Curtin 1986a, Mississippi River plume – 6–8 km wide: Grimes & Finucane 1991). The position of the front may change greatly with state of the tide, freshwater input and mainstream currents. Plumes from rivers such as the Amazon always have a high freshwater discharge and, therefore, always have a strong density gradient. The density structure of plumes is a combination of variation in salinity and temperature (Grimes & Kingsford 1996). Rivers that do not have 247

248

600–1200 summer

600–1600 summer 800–4000 summer

600–1600 mixed 600–3200 mixed 300–800 winter

Timor Sea (539 000)

Gulf of Carpentaria (640 800) Northeast coast (454 000)

Central-Southeast coast (268 000) Tasmania (68 400) South Australia (75 370) Southwest coast (140 000) West coast (520 000)

400 –1200 winter 200– 400 arid

Annual rainfall (mm)

Region area (km2)

0.9–2.5 0.5–2.0 0.4 0.5–5.8

0.007

0.052

0.012

1.6–6.3

0.183

0.690

2.2–7.7

0.099

1.1–1.8

2.0–10.5

0.138

0.136

Tidal range (m)

Runoff per km2 (× 1010 m3) Macrotidal drowned river valleys, mature (prograding coastal plains) Macrotidal drowned river valleys, (prograding deltas) Barrier; Drowned river

<1 <1

Barrier Barrier Drowned river

>2.5 >2.5 >2.5 central 1–2.5 north

1–2.5

Barrier; Drowned river and delta type Drowned river

1–2.5

<1

Dominant morphological estuary type

Wave energy (m)

Dry: highly stratiﬁed /saline; Wet: freshwater ﬂush Permanent inverse circulation

No data

Dry: inverse estuary; Wet: short freshwater ﬂushes Dry: inverse estuary; Wet: short ﬂushes Dry: well mixed; Wet: highly stratiﬁed during ﬂoods Well mixed, short periods of stratiﬁcation after ﬂoods Salt wedge

Seasonal mixing regimes

Table 2 Environmental factors inﬂuencing distribution of types of estuaries around Australia (modiﬁed from Kench 1999). Many of these environmental factors will also inﬂuence the effects that freshwater discharge will have on habitats and their associated organisms.

BRONWYN M. GILLANDERS AND MICHAEL J. KINGSFORD

249

30

111–185

S

L

Burdekin

Congo

Australia

DR Congo

741

ND

ND

−0.3–0.3

Chesapeake Bay M S Botany Bay, Cooks, Georges S Seiont

USA Australia

Wales

≈100

≈8

M

Rhone

France

165 2–10

<1 5

≈90 60

50 10

M M

Ebro Connecticut

Spain USA

50–130 −2–10

20–30

11

0.8

0.8

20 1

≈1

400

500 <800 <100

120–200 <100 30– 40

L L M

Amazon Mississippi Fraser

Brazil USA Canada

Width of the mouth (km)

Distance along the coast (km)

Size Distance of front from mainland (km)

River/Bay

Country

Depth of plume within 10 km of front (m)

ND

ND

≈0.4 45 000

0.3–1

ND 1–4

ND

10–15 1

5

ND ND

500–5000

ND 560

1–10 200 000 10–12 18 300 1000–8000 ND

Flow of water (m3 s−1)

ND

5

0.6

ND 14

2–3

ND ND

80–100 2–20 ≈10

80

100–120

ND

65–120 25–50

50

45 Sound

200–300 50 Sound

Curtin 1986a Gunter 1979 Mackas & Louttit 1988 Sabates 1990 Garvine & Monk 1974, Garvine 1986 Leveau et al. 1990, Sabates 1990, Sourna et al. 1990 Garvine 1986 Kingsford & Suthers 1994 Simpson & Nunes 1981 Thorrold & McKinnon 1992 Eisma & Van Bennekom 1978

Width of Reference Zone in shelf (km) which the front is located (km)

Table 3 Dimensions of river discharge plumes (taken from Grimes & Kingsford 1996, published with permission). Sizes of plumes were S, small (<20 km from shore and ﬂow <100 m3 s−1); M, medium (21–50 km and 101–5000 m3 s−1); and L, large (>50 km and 5000 m3 s−1). The distance from the mainland is calculated from the mouth of rivers or bays. Negative values refer to fronts positioned up rivers or in bays. A range is often presented because the position of plume fronts can vary with time. Where plumes are generated in sounds between the mainland and islands, the width of the shelf is not given. Most details are from sources, but some distances were estimated from maps where data were not available.

FRESHWATER EFFECTS ON ESTUARINE AND COASTAL HABITATS

BRON W YN M . GI LLANDERS AND MI C H A E L J. K I N G S F O R D

large drainage basins may have a low-density layer originating from solar heating (Kingsford & Suthers 1996). Plumes are generally characterised by higher levels of nutrients than surrounding oceanic waters and complex circulations that may facilitate the retention of particles including larval forms (Grimes & Kingsford 1996). Plumes have important roles in providing nutrients to coastal waters (especially where the ocean is oligotrophic). The recruitment of organisms such as prawns and ﬁshes varies with riverine ﬂow and associated size of plumes through variation in conditions for feeding, retention of larvae and abundance of predators (Grimes & Kingsford 1996). Fishes and prawns could use plumes to navigate to rivers, including natal rivers, particularly late stage larvae (Kingsford & Suthers 1994) and adult ﬁshes, such as salmonids (Quinn & Dittman 1990). The “fallout” of sediment from plumes is also a signiﬁcant input of detritus and sediment to deposit feeders on continental shelves and has a great inﬂuence on soft sediment habitats (Harris et al. 1996). Therefore, variation in freshwater input and the nature of plumes has an impact on pelagic and benthic habitats of shelf waters and has great potential to affect the productivity of ﬁsheries (see also p. 285).

Geomorphology The geomorphology of estuaries is affected by river discharge and, in turn, the geomorphology inﬂuences possible impacts from fresh water. In coastal plain estuaries there is an open entrance and plumes of freshwater discharge can extend offshore. By comparison, in blind and bar-built estuaries, water ﬂow is likely to be restricted to within the estuary. There can be a considerable build-up of freshwater during ﬂoods in this type of estuary. Marine processes may penetrate large distances up coastal plain estuaries depending on tide and wave energy (Kench 1999). Although estuaries have been categorised on geomorphological grounds and intuitively this should affect organisms, there is little empirical information to indicate whether these classiﬁcations have biological meaning (Edgar et al. 2000). Prominent sand bars form at the entrance of many estuaries and form either barrier estuaries or saline coastal lagoons (Kench 1999). These systems may become closed either seasonally or for longer periods (Lukatelich et al. 1987, Pollard 1994, Young et al. 1997). In seasonally closed estuaries of Western Australia, for example, sand bars typically form during the dry summer and autumn period and are breached in the late winter or early spring, when freshwater discharge increases markedly and scours a channel to the sea (Lukatelich et al. 1987, Hodgkin & Hesp 1998). The bars of some estuaries are now artiﬁcially breached because freshwater impoundment and associated sediments have prevented natural breaching. Hence, there is reduced scouring of the bar and marked siltation of the channel (e.g. Tuggerah Lakes in NSW, Wilson Inlet in Western Australia: Lukatelich et al. 1987). Most of the artiﬁcial breaching of coastal lagoons in NSW is to prevent ﬂood damage of properties. Many estuaries may close more frequently and for longer periods than in the past (Schlacher & Wooldridge 1996). Seasonal and annual variation in storms (and associated swell) and river discharge may determine whether the mouth of an estuary is open or closed and hence whether it is available for the recruitment and emigration of marine invertebrates and ﬁshes.

Temperature Alterations to freshwater input may decrease the temperature of the upper layer of the water column. Regulation of freshwater ﬂow may have caused a 0.5°C decrease in surface 250

FRESHWA TER EFFECTS ON ESTUARI N E A N D C O A S T A L H A B I T A T S

temperature in the Gulf of St Lawrence (Sinclair et al. 1986). There can also be increases in temperature if shallow river waters are warmed during summer and then enter cooler ocean waters, for example, in regions of upwelling. There are few reports of the effects of changes in freshwater ﬂow to estuaries on temperature.

Salinity Well-deﬁned salinity gradients characterise most estuaries and lagoons where salinity is low near the river mouth increasing to seawater salinity near the connection to the sea or beyond the estuarine plume. In addition, there are areas in estuaries where the waters are vertically stratiﬁed with lower surface salinity relative to bottom salinity (e.g. Brook 1982). The lighter plume water ﬂows over the heavy ocean water creating a characteristic hydrographic structure (Fig. 6). If the mouths of rivers are shallow and the runoff high, then the nearsurface salt water is pushed outward creating a wedge shape (Mann & Lazier 1991). The position of salinity wedges changes with the strength of ﬂow in the upper layer and the state of the tide. If there is abstraction of water upstream the extent of saltwater intrusion into estuaries can change. For example, it was estimated that doubling or halving the current water extraction in the Richmond River catchment (NSW, Australia) would change the saltwater intrusion distances by 2 km to 5 km (Water Research Laboratory, University of New South Wales, unpubl. report). Variation in salinity near the mouth of a creek or river varies on a tidal basis. For example, low-salinity brackish water (10) may be found in tidal creeks near low tides, whereas salinity can reach 28 near high tide (see Fig. 5 in Kitheka 1996). Low-salinity water

Figure 6 Density contours (sigma-t) with depth. The x-axis is a proﬁle from the middle of Botany Bay, Australia to the entrance (2.5–3 km) and beyond. The approximate position of the front was 7.5 km from where the transect commenced. The bottom topography is shown by the dark area.

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BRON W YN M . GI LLANDERS AND MI C H A E L J. K I N G S F O R D

can also be dispersed over a wider area during spring tides where currents are stronger than during neap tides. Where there is strong seasonal freshwater inﬂow, salinity can be lower (2) during the wet season but hypersaline during the dry season as a result of evapotranspiration (Kitheka 1996). By reducing freshwater inﬂow the salinity of the water column may be increased especially where freshwater inﬂux is the major factor causing wide salinity changes (e.g. Kitheka 1997).

Sediments Turbidity results from particulate material held in suspension, which may be due to algal blooms or to the natural inorganic sediments. Consistent freshwater inﬂow transports suspended sediments and debris that may increase the turbidity of the water column and reduce availability of light (Adams et al. 1992), although turbidity will depend on the source and sediment that the river ﬂows over. By comparison, turbidity can be particularly great especially after large storms or pulse events. Reduced freshwater inﬂow may lead to increased stability of sediments and/or larger sediment volumes in estuaries than under natural ﬂow regimes, which may increase the frequency and length of time in which the mouths of some estuaries are closed (Schlacher & Wooldridge 1996). Mechanical barriers that form when freshwater discharge is relatively low include sand bars. Build-up of sediment in the lower reaches of estuaries impedes tidal exchange between the sea and the estuary, changing water chemistry and trapping nutrients, leading to eutrophication (Schlacher & Wooldridge 1996, see p. 250). Reduced freshwater inﬂow may also lead to reduced sediment in estuaries because, as velocity of ﬂow diminishes, deposition increases. Sediment that would normally be transported to estuaries builds up in low ﬂow areas such as dams and along rivers (see Nile River and Yellow River case studies pp. 244–245). If the river can supply sediments faster than they can be reworked by the waves, then a shallow offshore proﬁle may develop thus limiting wave power in the vicinity of the river mouth and building a protruding delta (Wright & Coleman 1973). If there is no freshwater input, then there may be increased erosion of the delta, such as with the River Nile delta. Eventually, this delta may erode so much that saline lakes may change to coastal bays (Stanley & Warne 1993).

Nutrients Freshwater inﬂows, precipitation, drainage from wetlands, sewerage systems and agriculture, as well as tidal exchange all carry nutrients (carbon, nitrogen and phosphorus) to estuaries. Freshwater inﬂow was the dominant source of nutrients in a Texas (USA) bay (Funicelli 1984). Fluctuations in nutrients from rivers are the basis of so called “bottom-up” models where nutrients affect the abundance of producers and consumers in estuaries and coastal waters. Fluctuations in nutrients are important for determining the abundance of jellyﬁshes, crustaceans and ﬁshes (Grimes & Kingsford 1996, Kingsford et al. 2000). Reduced freshwater ﬂows to estuaries would, therefore, directly reduce nutrients.

Dissolved oxygen Low oxygen is not usually a problem in well-mixed estuarine and coastal waters. If primary production, nutrient concentrations or organic loading become excessive, however, bottom 252

FRESHWA TER EFFECTS ON ESTUARI N E A N D C O A S T A L H A B I T A T S

waters may become hypoxic (i.e. oxygen depleted), especially if the water column is stratiﬁed by temperature and salinity (Sklar & Browder 1998). Sinking and degradation of organic material (e.g. phytoplankton) creates a high biological oxygen demand. If the water column is stratiﬁed then mixing of oxygen-deﬁcient waters near the bottom with oxygenated surface waters is very slow. Hypoxia may become so severe that it causes widespread mortality of benthic macrofauna and ﬁshes. Elevated riverine discharge may exacerbate hypoxic/anoxic conditions. A recent ﬂood in northern NSW, Australia (2000, Clarence River) resulted in anoxic conditions due to the decomposition of large quantities of plant material. These conditions resulted in catastrophic losses of ﬁshes and invertebrates (R. Creese, NSW Fisheries, pers. comm.). During a period of record freshwater ﬂow of the Mississippi River there was a severe depletion of bottom-water dissolved oxygen in offshore waters that covered a much larger extent than the usual seasonal depletion (Rabalais et al. 1998). Similar relationships have also been found in other parts of the world (Justic et al. 1993). Alterations to water quality and freshwater inﬂows (e.g. increased ﬂows from ENSO events, p. 240) will ultimately change the frequency, extent and duration of hypoxic events (Justic et al. 1993).

Contaminants Direct introduction of contaminants to coastal waters is often related to activities in catchments (see also p. 240). Fine-grained estuarine sediments may sequester contaminants. Upon entering the estuarine environment most contaminants partition from solution/suspension and bind with suspended silts, clays and organic particulates before being bedded (Kennedy 1984, cited in Coull 1999). Many ligands may disassociate and form complex molecules according to salinity. If the salinity wedge changes position along an estuary, the chemical nature of sediments will also change. For example, cadmium is remobilised from particle surfaces by increasing salinities and may become more bioavailable with a reduction in freshwater input to an estuary (Schuchardt et al. 1993). This increased bioavailabity could affect assemblages associated with soft sediment habitats such as detrital-feeding organisms that accumulate contaminants and then pass these along the food chain (see also section on invertebrates, p. 271). The redox potential of the sediment, temperature and pH also affect complexation and binding of metals. Concentrations of free ions may vary greatly according to ﬂow of freshwater into coastal systems (with associated natural concentrations and elevated anthropogenic contaminants) and this variation can be recorded in body tissues (ﬁshes, Hanson & Hoss 1986) and the carbonate of organisms including corals (McCulloch et al. 1996, Alibert & McCulloch 1997) and ﬁshes (Gillanders & Kingsford 1996, Thorrold et al. 2001). Small amounts of acid that discharge into an estuary are generally neutralised by its alkalinity but after ﬂood events, the alkalinity of estuaries is reduced and acidiﬁcation of tidal reaches can occur for signiﬁcant periods (e.g. >7 wk, Sammut et al. 1996). Acidiﬁed waters may have lethal (e.g. massive ﬁshes kills) and sub-lethal effects on ﬁshes and invertebrates (see also p. 282). Native aquatic macrophytes (see also p. 264) may also be destroyed and replaced by acid-tolerant species such as Nymphaea spp. and Eleocharis spp (Sammut et al. 1995). Physical attributes of the water column (e.g. salinity, temperature, dissolved oxygen) and the substratum (e.g. sediment size, detrital loads) are affected by freshwater input. Physical changes generally mean a change in the nature of habitats (see effects on habitat-forming 253

BRON W YN M . GI LLANDERS AND MI C H A E L J. K I N G S F O R D

species below), which may then alter the distribution and abundance of individual species, as well as assemblages of organisms.

Effects on habitat-forming species Salt marsh Salt marsh habitats of grasses, herbs or low shrubs, are one of the highest intertidal habitats extending up estuaries to the limit of tidal inﬂuence (Vernberg 1993, Connolly 1999). Unvegetated pans may also form part of marshes. Salt marshes range in salinity from brackish to hypersaline depending on the amount of freshwater input and the extent of inundation by sea water at high tide. They are found throughout the world in mid- and high latitudes with different regions (e.g. northern Europe, Arctic region, Mediterranean) characterised by different types of vegetation (Chapman 1974). Australian salt marshes have shorter and less frequent periods of inundation than Northern Hemisphere marshes and, therefore, are likely to be greatly affected by reduced freshwater ﬂows. Salt marshes depend on freshwater supply and tidal exchange of water, thus preventing the formation of hypersaline sediments and ensuring ﬂushing of marshes (Dixon & Florian 1993, Schlacher & Wooldridge 1996). Altering these exchanges may seriously threaten the long-term viability of salt marsh habitats. For example, ﬂood mitigation structures may drain salt marsh (and mangroves) outside of ﬂood periods resulting in loss of this habitat in some regions (Pollard & Hannan 1994). Low discharge may produce nutrient deﬁciencies and excessive soil oxidation, whereas high discharge can produce abrasive ﬂows and waterlogging (Sklar & Browder 1998). Although salt marshes are likely to be greatly affected by alterations to ﬂow and its effects on various physical variables, relatively little is known on responses of salt marshes to these variables (see also mangroves below). Reduced ﬂows to marshes could affect other organisms if they are no longer able to use marsh habitats due to reduced inundation.

Mangroves Mangroves are found in sheltered estuarine habitats and along the banks of the rivers feeding these estuaries, where they may form dense intertidal forests. Extensive areas of mangroves are typically found in estuaries of large rivers that run over a shallow continental shelf (e.g. mouths of the Ganges and Brahmaputra rivers in Bangladesh, Mekong delta in Vietnam: Tomlinson 1986). There are two main centres of mangrove diversity, namely the eastern and western hemispheres; the eastern group has a larger number of species (Tomlinson 1986). The number of species may also vary with latitude. Within Australia, for example, there is only one species (Avicennia marina or grey mangrove) found in southern regions (e.g. southern Western Australia, South Australia, Victoria), two species (A. marina and Aegiceras corniculatum or river mangrove) in southern New South Wales, with numbers increasing northwards along the Queensland coast to between 30 and 35 species in the tropics (Chapman & Underwood 1995). 254

FRESHWA TER EFFECTS ON ESTUARI N E A N D C O A S T A L H A B I T A T S

Floodgates have greatly reduced the tidal penetration into mangrove habitats resulting in complete loss of this habitat in some areas (e.g. lower reaches of the Clarence River system, NSW, Australia: Pollard & Hannan 1994; see also case study on Ganges River, p. 246). Although salt water was able to penetrate upstream of the ﬂoodgates over the longer term, the amount of tidal exchange was inadequate. Tidal exchange not only allows ingress of saline estuarine water but also allows propagules (e.g. mangrove seeds and seedlings) of a wide variety of species to move upstream (Pollard & Hannan 1994). Decomposition of mangrove leaf litter was also reduced on the landward side of ﬂoodgates compared with the seaward side (Dick & Osunkoya 2000). Prolonged inundation of habitats, where water levels are increased above mean levels may lead to changes in the structure of wetlands. For example, a wetland that was ﬂooded for 8 wk in Brunei showed changes in the structure of assemblages of plants and animals (Choy & Booth 1994). Pneumatophores that were submerged died back and shrub-sized mangroves also died (Choy & Booth 1994). Increased salinity from reduced freshwater ﬂow also has a negative impact on mangroves (see case study on Ganges River, p. 246). The species composition of mangroves may be inﬂuenced by salinity. For example, Avicennia marina can tolerate higher salinities than Aegiceras corniculatum and is likely to colonise further inland if the marine inﬂuence of the estuary increases due to lack of freshwater ﬂows. Optimum growth of Avicennia marina occurred when exposed to salinities of 20 (Naidoo 1987). The effects of changes in other physical factors (e.g. nutrients, dissolved oxygen) on mangroves are not well known.

Seagrasses Seagrasses are generally restricted to depths <30 m, although the maximum depth is likely to depend on light availability (see Duarte 1991). They can reach depths of 45 m in clear water, but in shallow, turbid-water estuaries the maximum depth may only be 2 m (McComb et al. 1981). Habitat-forming Zostera and Posidonia are rarely found in high densities below 10 m in Australia. Seagrasses typically occur in areas of reduced water ﬂow (McComb et al. 1981). They tolerate salinities between 2 and 46, although these limits may only be tolerated for short periods of time (McComb et al. 1981). Coastal rivers and estuaries also exhibit gradients in salinity and thus a variety of different species of seagrass may be found along this gradient. In eastern Australia, for example, high salinity regions of coastal rivers and estuaries have areas of Zostera capricorni (West et al. 1989). In more brackish waters, beds of Ruppia megacarpa and Vallisneria gigantea are found. The leaf surface of seagrass may have a heavy load of epiphytes including bacteria, algae, hydroids, ascidians, sponges and foraminiferans. Epiphyte loads can be an indicator of poor water quality and also of poor circulation or ﬂushing, which is related to freshwater ﬂow. Seagrass (as well as marsh and mangrove) habitats are often cited as nursery and feeding areas for invertebrates and ﬁshes, but they may also trap silt and wastes from upland runoff, and stabilise the shoreline (Short & Wyllie-Echeverria 1996). Although numerical models suggest that seagrass beds enhance sedimentation and reduce re-suspension, there is little experimental evidence to support this suggestion (but see Terrados & Duarte 2000). Most species have more or less regularly branching horizontal rhizomes that creep over or in the sediment and bear apical shoots with several straplike, linear leaves (Vermaat et al. 1996). A different leaf form is found in two genera, Syringodium with subulate leaves and 255

BRON W YN M . GI LLANDERS AND MI C H A E L J. K I N G S F O R D

Halophila with ellipsoid leaves. Many genera (e.g. Cymodocea, Halodule, Posidonia, Syringodium, Thalassodendron and Thalassia) produce vertical or short shoots on erect stems that allow these seagrasses to respond to burial by vertical elongation (Vermaat et al. 1996). Other genera rearrange the position of the horizontal rhizomes (e.g. Enhalus, Halophila, Zostera) or attain considerable size (e.g. Enhalus) to survive in environments with high sedimentation loads (Vermaat et al. 1996). In mixed beds the smallest species with lower leaf canopies may be expected to suffer most. Sediment depth, water temperature, water salinity, water clarity or light availability, water motion and nutrients are important for the development and growth of seagrass beds (Powell et al. 1989). All these factors will be inﬂuenced by management practices in catchments and changes in the amount of fresh water entering estuaries and marine systems. A predictive ecosystem model has been developed for the seagrass community in South Florida (USA) that enables managers to predict the effects of various scenarios of freshwater ﬂow on the seagrass ecosystems (Fong & Harwell 1994, Fong et al. 1997). Fong & Harwell (1994) incorporated ﬁve biotic variables into their model including three common species of seagrass (Thalassia testudinum, Halodule wrightii and Syringodium ﬁliforme), epiphytic macro- or micro-algae and rhizophytic macroalgae. In addition, ﬁve environmental variables (temperature, salinity, light, concentration of water column phosphorus (P) and sediment P concentration) were identiﬁed as important in controlling biomass and relative abundance of the biotic variables and were included in the model. The model was tested by sensitivity analysis and validation experiments, and predicted temporal changes in seagrass communities relatively well for different environmental conditions over a 1-yr period (Fong et al. 1997). Enormous changes in seagrass assemblages were predicted if the freshwater input to the system was increased, especially if the fresh water carried nutrients (Fong et al. 1997). Such a model may be a useful predictor of different ﬂow scenarios but after changes in freshwater input have been made, the model should be veriﬁed by ground truthing. Seagrasses survive in a wide range of salinities, although there are few studies that directly test the effect of altered salinity on seagrasses (Table 4) (Montague & Ley 1993, Fong & Harwell 1994). Most data relating to salinity are correlative and show that seagrasses are generally tolerant to changes in salinity. Seeds may not germinate over the range of salinities in which growth occurs. Ruppia spp. grows vegetatively over salinities from freshwater to hypersaline (i.e. a salinity of 75), but seeds did not germinate at salinities above 55 (Adams & Bate 1994). If freshwater input to estuarine waters is limited to the extent that waters become hypersaline then a freshwater pulse may be required to lower salinity and promote germination. In contrast, Zostera capensis has a narrower salinity tolerance but can also survive under freshwater conditions, although biomass and density are reduced over time in freshwater (Adams & Bate 1994). Negative effects of salinity ﬂuctuations could be greater at low salinity because many marine species cannot tolerate freshwater for long (Montague & Ley 1993). Benthic plants are likely to be inﬂuenced not only by the frequency and amplitude of changes in salinity (and other factors, e.g. light and turbidity), but also by its suddenness, seasonal timing, and frequency (Montague & Ley 1993). Salinity changes may alter the distribution and abundance of seagrasses (Adams & Talbot 1992, Montague & Ley 1993, Kamermans et al. 1999). When freshwater input is reduced (e.g. from river impoundment), the salinity may become more stable and seagrass may colonise upper estuarine areas. A mathematical model predicted that with reduced freshwater inﬂow and associated salinities that were more stable, Zostera would move into the upper reaches of the estuary and displace brackish and freshwater macrophytes, including salt 256

Type of study

257

Zostera capricorni

Higher temperature – faster Experimental, laboratory aquaria, germination, seedling development only between press 17°C and 25°C Rapid germination at 16°C Experimental, laboratory aquaria, (winter), low levels at 26°C (summer) press

Seedlings – shoot survivorship Experimental, laboratory aquaria, declined with increasing burial depth; growth increased if press burial <7 cm

Cymodocea nodosa

Water temperature Cymodocea nodosa

Experimental, ﬁeld study, pulse

Diversity and biomass declined as % silt and clay of sediment increased Diversity and shoot density decreased with increased amounts of suspended material Shoot density, vertical growth and branching – response varied by species

Effect

Mixed seagrasses

Seagrasses (7 species) Correlative, ﬁeld study

Sediment Seagrasses (7 species) Correlative, ﬁeld study

Factor/Species

Cote D’Azur, France, 43 N, 7 E (collection site) Queensland, Australia, 27 S, 153 E (collection site)

NE Spain, 43 N, 4 W (collection site)

Philippines, 16 N, 119 E

Thailand and Philippines, 5–15 N, 100–125 E Philippines, 16 N, 119 E

Location Lat. & Long.

Temporal design

6 temperatures; 20 L aquaria, 3 replicates of 10 seeds 2 temperatures × 2 salinities – 3 replicates of 25 seeds; 9 cm diameter dish

Caye & Meinesz 1986

Brenchley & Probert 1998

Monitored every 7–14 days for 372 days

Marba & Duarte 1994

Duarte et al. 1997

Bach et al. 1998

Terrados et al. 1998

Reference

Experimental duration 30 days; monitored <10 ×

April–May 1995, March–April 1996, sampled once/site 3 months, June– 4 sites along a silt gradient, separated August 1995; one-off sampling by 0.1° latitude May 1995–March 5 burial levels, 3 1996 experimental random blocks duration; sampled separated by 3 times (but only 10–15 cm with 3 replicates per block 1 replicate each time) 8 sediment levels, 7 Experimental replicate seedlings, duration 35 days before harvesting 2 aquaria 5 locations, 7–35 sites/location

Spatial design

Table 4 Factors inﬂuencing development and growth of seagrasses. The type of study indicates whether the study was correlative or a manipulative experiment, whether it was conducted in the ﬁeld or laboratory and whether it was of a press or pulse nature. In correlative studies it was generally not possible to determine the type of disturbance (press or pulse) that had occurred. Not every study is included. Within each section, the table is ordered by location from north to south.

FRESHWATER EFFECTS ON ESTUARINE AND COASTAL HABITATS

258

Experimental, Germination slower at high laboratory aquaria, salinity than low salinity in press aerobic conditions, no difference under anaerobic conditions Experimental, Survived and grew best laboratory aquaria, between 15 and 35 press

Experimental, Survived and grew best in laboratory aquaria, freshwater press

Ruppia cirrhosa

Zostera capensis

Location Lat. & Long.

continued

South Africa, 33 S, 24 E (collection site)

South Africa, 33 S, 25 E (collection site)

Queensland, Australia, 27 S, 153 E (collection site)

Cote D’Azur, France, 43 N, 7 E (collection site) Experimental, Increased above and below Lake Grevelingen, laboratory aquaria, ground biomass at 22 than 32; Netherlands, press reduction in photosynthesis at 52 N, 5 E high salinity 100% mortality of (collection site) Experimental, marine plants when grown in Netherlands laboratory aquaria, 20, estuarine plants survived 53 N, 5 E press (collection site)

Effect

Experimental, Germination and survival laboratory aquaria, increased at lower salinities press

Type of study

Zostera capricorni

Zostera marina

Zostera marina

Salinity Cymodocea nodosa

Factor/Species

Table 4 Temporal design

6 salinities; 20 L Experimental aquaria, 3 replicates duration 50 days, of 10 seeds monitored every 2 days 2 salinities, 3 Monitored every cylinders (30 cm 7–10 days for diameter × 50 cm 65–119 days height)/treatment 3 salinity Sampled 2 and treatments, 1 75 ml 5 wk after glass container/ treatments treatment commenced 2 temperatures × 2 Monitored every salinities – 3 7–14 days for replicates of 25 372 days seeds; 9 cm diameter dish 5 salinity Experimental treatments, 1 17 L duration 3 months, tank/salinity mortality assessed weekly, other factors after 3 months 5 salinity Experimental treatments, 1 17 L duration 3 months, tank/salinity mortality assessed weekly, other factors after 3 months

Spatial design

Adams & Bate 1994

Adams & Bate 1994

Brenchley & Probert 1998

van Katwijk et al. 1999

Kamermans et al. 1999

Caye & Meinesz 1986

Reference BRONWYN M. GILLANDERS AND MICHAEL J. KINGSFORD

Observational – literature review

259

Experimental, ﬁeld study, pulse

Halophila ovalis

Density reduced during shading, no recovery after shade screens removed

Experimental, Biomass decreased during laboratory aquaria, light deprivation press and pulse

Biomass and density decreased in shaded treatments

Halophila ovalis

Halodule pinifolia and Halophila ovalis

Growth rate and biomass increased with increasing light

Experimental, outdoor mesocosm, press Experimental, ﬁeld study, press

Zostera marina

Zostera marina

Experimental, ﬁeld Mortality of plants in low study, press light treatments, decreased productivity and growth Experimental, Growth decreased with microcosms in increased shading glasshouse, press

Thalassia testudinum

Correlation between colonisation depth and light attenuation Seagrasses (7 species) Experimental, ﬁeld Inconsistency among species study, press

Water clarity (light) Angiosperms

Queensland, Australia, 27 S, 153 E

Queensland, Australia, 27 S, 153 E (collection site)

Little Bay, USA 40 N, 73 W (collection site) Queensland, Australia, 17 S, 140 E

Chesapeake Bay, USA, 37 N, 76 W (collection site)

Texas, USA 27 N, 97 W

1 site, 3 control and July–August 3 shaded plots, each 1995; sampled during 2 periods plot 1.5 m2 ≈2 wk apart 3 light treatments, 3 Quarterly replicates (1.5 m2)/ measurements for treatment 490 days 3 light × 2 nutrient Monitored every treatments with 4 2 wk for 20– replicates of each, 50 days, repeated 110 L aquaria in summer, fall, spring 6 light treatments, 1 Monitored every treatment per 0.8 m3 other month over outdoor tank 4 months 1 site with shaded Monitored every and non-shaded 7–14 days for treatments, 3 78 days replicates/treatment, shades 2.2 m diameter 3 light treatments, Monitored every 4–6 200 L outdoor 3–14 days for aquaria (2–4 24–32 days replicate aquaria/ treatment), 3 replicates sampled 2 water depths with Monitored before shaded and nonshades removed shaded treatments, and 18 days after 3 replicates (1.6 m shades removed; diameter)/treatment, experimental shades removed duration 32 days after 14 days Philippines, 16 N, 119 E

Studies published 1954–1989

20 countries

20 countries

Longstaff et al. 1999

Longstaff et al. 1999

Longstaff & Dennison 1999

Short et al. 1995

Moore & Wetzel 2000

Lee & Dunton 1997

Bach et al. 1998

Duarte 1991 FRESHWATER EFFECTS ON ESTUARINE AND COASTAL HABITATS

260

Zostera marina

Halodule wrightii

Nutrients (water column and/or sediment) Thalassia testudinum

Water motion Thalassia testudinum

Factor/Species

Seagrass standing crop increased after 1 yr but after 3 yr ns; root and rhizome biomass similar; length and width of blades longer or wide

Highest biomass and largest blade area at intermediate ﬂow rates

Effect

Florida, USA 25 N, 81 W

Florida, USA, 24 N, 80 W (collection site)

Location Lat. & Long.

continued

Shoot density and biomass Experimental, outdoor mesocosm, decreased with increasing nutrients press

Little Bay, USA 40 N, 73 W

Florida, USA Seagrass standing crop, root Experimental – and rhizome biomass increased; 25 N, 81 W addition by seabirds defecating blades longer

Experimental – addition by seabirds defecating, press

Experimental, laboratory microcosms, press

Type of study

Table 4

5 sites spread over 3100 m, experimental and control treatment at each site, 4 replicates at each site 5 sites spread over 3100 m, experimental and control treatment at each, 4 replicates at each site 6 0.8 m3 outdoor tanks; paired tanks with one control and one enriched with N and P

3 hydrodynamic conditions, 1 microcosm/ treatment, Outdoor tanks, 6 L SW in microcosms

Spatial design

Powell et al. 1989

Powell et al. 1989

Short et al. 1995

Sampling once, twice or annually over three year experimental duration Sampled monthly for 4 months

Koch 1999

Reference

Sampling once, twice or annually over three year experimental duration

2 months equilibration, monitored additional 4 months

Temporal design

BRONWYN M. GILLANDERS AND MICHAEL J. KINGSFORD

Queensland, Australia, 16 S, 145 E Queensland, Australia, 27 S, 153 E Queensland, Australia, 27 S, 153 E Queensland, Australia, 27 S, 153 E

Experimental, ﬁeld Biomass and growth not study, press signiﬁcantly increased by N or P

Experimental, ﬁeld study, press

Experimental, ﬁeld study, press

Correlative, ﬁeld study

Syringodium isoetifolium

Cymodocea serrulata

Zostera capricorni

Zostera capricorni

Growth and biomass not affected by nutrient additions to sediment Biomass and growth increased in response to N + P additions to sediment Nutrient content and amino acid concentrations higher at sites close to nutrient sources but growth and morphology not related to proximity to nutrient sources

Queensland, Australia, 16 S, 145 E

Experimental, ﬁeld Density and growth increased study, press with added N, no effect of P on growth

Halodule uninervis

2 sites <200 m apart, 4 treatments × 3 replicates, 1 m diameter areas 2 sites <200 m apart, 4 treatments × 3 replicates, 1 m diameter areas 4 treatments × 3 replicates, 1 m diameter areas 4 treatments × 3 replicates, 1 m diameter areas 9 sites spaced over 60 km; sites separated by 100s m – km Sampled early summer

Monitored after 92–94 days

Monitored after 92–94 days

Monitored after 80 days

Monitored after 80 days

Udy & Dennison 1997a Udy & Dennison 1997a Udy & Dennison 1997b

Udy & Dennison 1997a, Udy et al. 1999 Udy et al. 1999

FRESHWATER EFFECTS ON ESTUARINE AND COASTAL HABITATS

261

BRON W YN M . GI LLANDERS AND MI C H A E L J. K I N G S F O R D

marsh, resulting in reduced species diversity (Wortmann et al. 1998). Increased salinity in the Netherlands is thought to have contributed to the long-term decline of seagrasses (Kamermans et al. 1999). Light availability is one of the more important environmental variables controlling the distribution and abundance of seagrass. Light requirements of seagrasses vary among and within species but are high in comparison with algae, phytoplankton and terrestrial plants (Abal et al. 1994). An average requirement for seagrasses as a group has been calculated as 11% of surface light (Duarte 1991). However, many measurements of light are made over short periods and the effect of seasonal or pulsed changes in light availability from, for example, ﬂooding rivers have not been taken into account (Longstaff & Dennison 1999). Pulsed turbidity events from ﬂooding rivers may deprive light from seagrass habitats for periods of a few days to many weeks (Moore et al. 1997, Longstaff et al. 1999). Increased water turbidity and/or biomass of epiphytes on leaves of seagrasses can reduce light. Poor catchment management could contribute to seagrass loss by increasing turbidity and seagrass epiphyte loads, although a number of other anthropogenic factors (e.g. urbanisation, dredging and fertilisers) may also be important (Longstaff et al. 1999). The spectral composition of light is also important to seagrasses (and algae) with different species favoured by different spectral compositions. Changes in water quality due to changes in freshwater input will also inﬂuence the spectral composition. Two major ﬂoods in short succession, which resulted in pulses of turbid water, were thought to have caused the loss of over 1000 km2 of seagrasses in Hervey Bay (Queensland, Australia: Preen et al. 1995). Seagrasses vary widely in their tolerance to light deprivation. Some species survive for more than 5 months below their minimum light requirements whereas others only survive 1 month (Moore et al. 1997, Longstaff & Dennison 1999, Longstaff et al. 1999). Biomass and growth rate of shoots of Zostera marina decreased with decreasing light, suggesting that turbid freshwater plumes may have an effect on seagrass beds (Short et al. 1995). Similar patterns were found for Posidonia sinuosa (Gordon et al. 1994) and Halophila ovalis (Longstaff et al. 1999; and see Table 4). Responses of the seagrass, H. ovalis, to reduced light followed by a recovery period were complex, affecting not only ecological factors (biomass, growth) but also physiology (Longstaff et al. 1999). Turbid pulses of fresh water not only deprive seagrasses of light but also have high levels of sediment. A decline in species richness of seagrass has been predicted following a reduction in light availability and increased sedimentation rates associated with increasing siltation (Terrados et al. 1998; see Table 4). In correlative studies where siltation levels varied, species richness declined as the percentage of silt and clay in the sediment increased (Bach et al. 1998, Terrados et al. 1998). Sediments with <15% silt and clay content contained a number of species, whereas those with >20% silt and clay contained three or fewer species (Terrados et al. 1998). Differences in sensitivity of seagrasses were suggested showing that Syringodium isoetifolium and Cymodocea rotundata were most sensitive to siltation, followed by Thalassia hemprichii, Cymodocea serrulata, Halodule uninervis and Halophila ovalis. Enhalus acoroides was most resistant to siltation. In an experimental study, three species of seagrasses (Thalassia testudinum, Halodule wrightii and Syringodium ﬁliforme) were mechanically disturbed to simulate sediment disturbance and their recovery monitored (Kenworthy et al. 2000). Halodule and Syringodium recovered faster than Thalassia and it was predicted that recovery was slower in soft carbonate mud banks than in silicious sediments (Kenworthy et al. 2000). It has been suggested that the distribution and biomass of some species of seagrasses (e.g. Zostera capensis in South Africa) ﬂuctuate in response to ﬂooding because plants are highly 262

FRESHWA TER EFFECTS ON ESTUARI N E A N D C O A S T A L H A B I T A T S

sensitive to siltation and sediment scouring (Talbot et al. 1990, Adams & Talbot 1992). Seagrass cover was reduced to virtually zero in two estuaries in South Africa after a major rainfall and associated deposition of sediments (Talbot et al. 1990). Subsequent recovery varied with Halophila ovalis rapidly colonising both estuaries and Zostera capensis expected to take at least 4 yr (Talbot et al. 1990). Since construction of a dam 4 km above the tidal reach of the Kromme estuary (South Africa), there has been a 4-fold increase in total standing biomass of Zostera in the estuary because small and moderate ﬂoods (anything less than a 1 in 30 yr) removed their ﬂuvial deposits that smothered seagrass beds (Adams & Talbot 1992). Only heavy ﬂoods (1 in 15 yr frequency) would probably result in complete removal of seagrass beds, moderate ﬂoods (1 in 2–3 yr frequency) would deposit ﬁne muddy sediments resulting in reduced growth and/or mortality, whereas small ﬂoods (1 yr frequency) would temporarily impair growth (Talbot et al. 1990). Turbid pulses of freshwater therefore change a variety of environmental factors that subsequently affect growth, distribution and abundance of seagrasses. A mathematical model was developed to determine the long-term effects of impoundment of river systems on vegetative growth of Zostera capensis (Wortmann et al. 1997). If there was no freshwater impoundment it was predicted that there would be 100% loss of Zostera biomass after a ﬂood, whereas estuaries with river impoundment would see a 50% difference in biomass between pre- and post-ﬂood values because dams attenuate ﬂoods (Wortmann et al. 1997). Dry conditions were thought to decrease water levels, which would lead to exposure of submerged vegetation and consequent die back since submerged macrophytes are sensitive to water levels. Wastewater entering streams and coastal waters carries nutrients and seagrasses may respond to changes in both sediment-associated and dissolved nutrients (Larkum et al. 1989). Increased nutrients to the coastal zone have often led to the decline of seagrasses (Cambridge & McComb 1984, Silberstein et al. 1986, Abal & Dennison 1996), although sometimes they can increase seagrass biomass and distribution (Udy et al. 1999). Growth responses may be found only if the environment is nutrient limited (Powell et al. 1989). Increased nutrients often lead to increased epiphyte, phytoplankton and macroalgal growth (i.e. phytoplankton/ algal-dominated system), which reduces light availability. Declines in seagrasses have usually occurred in regions where large seagrass communities were present prior to anthropogenic impact, where nutrient availability was probably already relatively high (Udy et al. 1999). It was hypothesised that regions with insufﬁcient nutrients to support extensive seagrass meadows would establish and sustain seagrass meadows with the addition of nutrients from sewage, organic waste or regional sources (Udy et al. 1999). A positive or negative effect on seagrass biomass and growth may be found from increases in nutrient availability (see also Table 4). There is also likely to be an interaction with light. In turbid estuarine waters, where light availability limits seagrass growth, increases in nutrient availability may result in a further decline in seagrass growth and/or biomass (Udy et al. 1999). However, in oligotrophic water, where light does not limit growth of seagrasses, an increase in nutrient availability could result in an increase in seagrass growth and biomass (Udy et al. 1999). Any water management practices that decrease water ﬂow may also decrease nutrient supplies and therefore lead to decreases in seagrass productivity. Water circulation provides nutrients for seagrass growth (Powell et al. 1989). If there is restricted exchange of water with the open-ocean and limited circulation with modiﬁed ﬂow regime (as is the case in northeastern Florida Bay, USA) then overland freshwater input may be an important source of nutrients for seagrass production (Powell et al. 1989). Increases in seagrass productivity may result if water management practices increase water ﬂow into an area. Highest biomass 263

BRON W YN M . GI LLANDERS AND MI C H A E L J. K I N G S F O R D

of Thalassia testudinum was at intermediate ﬂow rates rather than stagnant or high ﬂow rates (Koch 1999), therefore some ﬂow is important. Contaminants such as herbicides that are carried in runoff may also affect seagrasses, although the effects may be further inﬂuenced by turbidity. Three herbicides had substantial impacts on the physiology of Halophila ovalis by reducing electron transport and affecting photosynthesis (Ralph 2000). Changes in ﬂow that concentrate contaminants or increase the amount of contaminants may have a substantial impact on productivity of seagrasses. Many of the factors that may inﬂuence mortality, recruitment and growth of seagrasses covary. For example, light levels, sediment load and salinity are likely to vary with levels of freshwater input, especially during storm-related pulses. It is difﬁcult, therefore, to separate these factors without controlled experiments. Responses of different species to factors affected by freshwater input may also vary among species of seagrasses. Caution should, therefore, be used before extrapolating among species.

Macroalgae Assemblages of macroalgae including the larger red, brown and green algae occur worldwide and may be a major component of shallow-water reefs. Factors similar to those inﬂuencing distribution and abundance of seagrasses probably affect algae (e.g. sediment, salinity, nutrients, light). Although there is little information on the effects of alterations in freshwater input on macroalgae, discharges and other activities that alter water quality are likely to have a major effect on large algae, particularly habitat-forming species. Many experiments were not aimed at determining the effects of freshwater input on algae, but their results may be applicable (Table 5). Effects of changes in freshwater ﬂow on algae may depend on the environment and whether the species is intertidal or subtidal. For example, if algae have been in an estuarine environment where there are frequent ﬂuctuations in freshwater input they may be more tolerant to ﬂuctuating levels than algae that have been in a stable, marine environment. Thus, it is unlikely to be simple to predict the effects of freshwater input on growth and survival of macroalgae. The response of a green macroalga (Codium tenue) to ﬂooding varies depending on whether it was present in intertidal or subtidal habitats (Talbot et al. 1990). In intertidal regions, C. tenue was removed by relatively low freshwater runoff, whereas subtidal populations required moderate freshwater runoff (Talbot et al. 1990). Major ﬂoods removed C. tenue from both intertidal and subtidal habitats. In the ﬁeld, algae are tolerant of a wide range of salinities and temperatures. For example, Enteromorpha occupies sites ranging in salinity from 7 to 23 (Rijstenbil et al. 1993, cited in Fong et al. 1996). Although tolerant to a wide range of salinities, experiments showed that growth was maximal over a more restricted range (15–20, Martins et al. 1999, see also Fong et al. 1996). Two species of green macroalgae were found to grow at their maximum rate at oceanic salinities but were negatively affected by low salinity (Fong et al. 1996). However, one species (Enteromorpha intestinalis) was affected less than the other species (Ulva expansa) and was favoured when salinities decreased directly after rain (Fong et al. 1996). Although species may be found over a wide range of salinities, growth rates may be reduced at low salinities and decreased biomass could make algae more vulnerable to herbivory. Growth of algae and herbivory are likely to be directly affected by increased salinity from restricting freshwater input to estuaries. 264

265

Enteromorpha intestinalis

Salinity Green algae

Ecklonia radiatia

Water temperature Macroalgae

Sargassum microphyllum

Turf-forming algae

2 treatments (reduced sediment and unmanipulated plots), 6 replicates of each, plots 16 × 20 cm separated by 1.5 m 2 areas, 7 treatments, 3 replicates 3 treatments, 5 replicate quadrats

Spatial design

California, USA 33 N, 117 W (collection site)

Portugal 40 N, 8 W (collection site)

Lowest growth at salinity <3, Experimental, laboratory, outdoor growth highest between 15 and 20 aquaria, press

3 salinities, 4 replicates, experimental unit (12 cm diameter × 5 cm depth) 5 aquaria of 100 L each separated into 5 sections

4 temperatures; 4 × 2 L glass aquaria placed in each of 4 growth chambers Temperature Northland and treatments 5–26°C, Wellington, NZ 36 and 41 S, 174 E other details not given (collection site) California, USA 33 N, 117 W (collection site)

Queensland, Australia 19 S, 146 E

Ligurian Sea, Italy 43 N, 10 E

Ligurian Sea, Italy 43 N, 10 E

Location Lat. & Long.

Increased growth with increase Experimental, laboratory aquaria, in salinity press

No growth between 6°C and Experimental, laboratory aquaria, 8°C, plants died at 26°C press

Maximum biomass at 18°C Experimental, laboratory aquaria, and 22°C press

Experimental, ﬁeld Abundance of turfs decreased study, press with higher deposition of sediments Experimental, ﬁeld Decreased recruitment, growth, survival with increased study, press amounts of sediment

Experimental, ﬁeld Sediment inﬂuenced structure study, press and diversity

Sediment Macroalgal assemblages

Effect

Type of study

Reference

Monitored daily for experimental duration of 6 days

Monitored at 8 days and 17 days, experimental duration 17 days

Biomass determined after experimental duration of 30 days Cultures grown for 8, 16 or 20 h, other details not given

Monitored 4 months after start of experiment Monitored 3, 9 and 15 months after setup

Martins et al. 1999

Fong et al. 1996

Novaczek 1984b

Fong & Zedler 1993

Umar et al. 1998

Airoldi & Virgilio 1998

Monitored monthly Airoldi & Cinelli 1997 for experimental duration of 1 yr

Temporal design

Factors inﬂuencing development and growth of algae. See Table 4 for further details.

Factor/Species

Table 5

FRESHWATER EFFECTS ON ESTUARINE AND COASTAL HABITATS

266

Effect

Florida, USA 30 N, 87 W

California, USA 33 N, 117 W (collection site)

Experimental, ﬁeld Rates of primary production, biomass and chlorophyll a study, press greater in enriched beds

Growth increased with Experimental, laboratory aquaria, addition of N pulse

Enteromorpha intestinalis, Ulva expansa

SA, Australia 35 S, 138 E

Epiphytes on 3 species of seagrass

Biomass increased with wave energy

3 nutrient treatments, 5 replicates, experimental unit (12 cm diameter × 5 cm depth)

2 treatments (enriched vs. control), 15 replicates spaced 1–3 m apart 2 enriched and 2 ambient plots per species

6 sites at 4 locations, 12–46 replicate quadrats

5 light levels, no replication of cultures

4 light levels, 20–25 replicates (laboratory); 2 light treatments, 20 replicates

NC, USA 34 N, 76 W

Northland, NZ 36 S, 174 E (collection site)

3 light levels, 4 aquaria on each shelf at each light level

Spatial design

California, USA 33 N, 117 W (collection site)

Location Lat. & Long.

Florida, USA 24 N, 80 W

Correlative, ﬁeld study

Growth rates increased with Experimental, laboratory aquaria, increasing light but at upper levels may be damaging press

Higher irradiance – increased Experimental, laboratory aquaria, biomass of ﬂoating macroalgae; lower light press levels – increased biomass of attached macroalgae Increased growth with high Experimental, laboratory, outdoor light microcosm and ﬁeld study, press

Type of study

continued

Experimental, ﬁeld Negligible effects of nutrient enrichment on abundance study, press

Nutrients Macroalgae

Water motion Cystophora, Sargassum

Ecklonia radiata

Dictyota ciliolata, Sargassum ﬁlipendula

Water clarity (light) Macroalgae

Factor/Species

Table 5

Novaczek 1984a

Cronin & Hay 1996

Fong & Zedler 1993

Reference

Wear et al. 1999 Sampled every 2 months from October 1993 to September 1994 Treatment day 1 and 7; experimental duration 4 wk

Fong et al. 1996

Miller et al. 1999 Monitored after 1 month and 2 months

Collings & 2 sites sampled twice, rest sampled Cheshire 1998 once

Biomass determined after experimental duration of 30 days Monitored after 11 days (laboratory); experiment repeated twice, monitored after 9 days and 14 days Monitored for 25 days

Temporal design

BRONWYN M. GILLANDERS AND MICHAEL J. KINGSFORD

267 Queensland, Australia 18 S, 146 E (collection site)

Experimental, No response to nutrient pulse laboratory, outdoor aquaria, pulse

Cladophora

Growth increased after Experimental, laboratory aquaria, addition of N and P pulse

WA, Australia 32 S, 115 E (collection site)

Queensland, Australia 18 S, 146 E (collection site)

Schaffelke 1999

Castel et al. 1996

Peckol & Rivers 1995

Boynton et al. 1996

Neckles et al. 1993

Treatments 2–3 wk, Gordon et al. experimental 1981 duration 5–7 wk

Pulse of fertiliser Schaffelke for 24 h, monitored 1999 for 14 h after pulse

Schaffelke Pulse of fertiliser for 24 h, monitored 1999 for 14 h after pulse

Pulse of fertiliser for 24 h, monitored for 14 h after pulse

3 lagoons

2 treatments (unfertilised and fertilized), 10 replicate 1 L plastic jars 2 treatments (unfertilised and fertilised), 10 replicate 1 L plastic jars 2 treatments (unfertilised and fertilized), 10 replicate 1 L plastic jars 5 treatments for each of P and N, 3 replicates , 2 L conical ﬂasks

Increased net photosynthetic Experimental, laboratory, outdoor rate with nutrient pulse aquaria, pulse

Chnoospora implexa Hydroclathrus clathratus Padina tenuis (ephemeral species) Chlorodesmis fastigiata Turbinaria ornata (perennial species)

Monostroma obscurum Correlative, ﬁeld study France, 43–44 N, 1 W–3 E Queensland, Australia 18 S, 146 E (collection site)

Monitored after 12 days experimental duration Months to years

3 nutrient treatments, outdoor tanks (30 L)

MA, USA 41 N, 70 W (collection site)

4 experiments initiated between June 1987 and April 1988; experimental duration 1–2 months Diel, monthly, seasonal

10s of km

2 treatments (ambient or enriched); 5 replicates, 110 L glass aquaria

Maryland, USA 39 N, 76 W

Chesapeake Bay, USA; 37 N, 76 W (collection site)

Rapid development of alga correlated with increased nutrient input Increased net photosynthetic Sargassum baccularia Experimental, laboratory, outdoor rate with nutrient pulse aquaria, pulse

Cladophora vagabunda, Gracileria tikvahiae

Correlative, ﬁeld study

Chlorophyll a

Epiphyte biomass increased with nutrient enrichment but grazers important

Chlorophyll a concentration increased with increasing annual total N load Experimental, Growth rates decreased in laboratory, outdoor elevated NH4+ treatment aquaria, press

Experimental, laboratory microcosms, press

Epiphytes on Zostera

FRESHWATER EFFECTS ON ESTUARINE AND COASTAL HABITATS

BRON W YN M . GI LLANDERS AND MI C H A E L J. K I N G S F O R D

The effect of fresh water on subtidal algae will depend on the depth of low salinity wedges. Kennelly & Underwood (1992), for example, found that inundation by fresh water leading to reduced salinities (∼5, Andrew 1991) resulted in death of kelp plants (Ecklonia radiata) to depths of approximately 3 m. Further changes in the algal communities resulted from the death of sea urchins, which graze on algae (Andrew 1991). Therefore, pulse events can have a major direct and indirect inﬂuence on algal assemblages in estuaries. Increased amounts of sediment in freshwater ﬂows can inﬂuence the structure and diversity of algal assemblages. Turf-forming algae decreased as sediment deposition increased (Airoldi & Virgilio 1998). Algae are likely to show similar effects to seagrasses such that different growth forms of algae will be affected differently by increased sedimentation. Recruitment, growth and survival decreased in Sargassum as the amount of sediment increased (Umar et al. 1998). Indirect effects (e.g. nutrients trapped in sediment, smothering of sessile invertebrate competitors, changes in the grazing ability of invertebrates) may also occur. Rain and riverine runoff may increase nutrients to estuarine waters and bays and promote macroalgal growth. Some macroalgae are able to grow at high concentrations of ammonia (e.g. untreated sewage efﬂuent), whereas others are intolerant of elevated NH4+ (Peckol & Rivers 1995). However, there has been a lack of manipulative ﬁeld experiments that test the effects of nutrient enrichment on macroalgae (Miller et al. 1999). The effects of nutrients from freshwater input on marine systems are akin to the controversy surrounding the effect of anthropogenic nutrient enrichment on coral reefs (see below). Most studies are observational (natural or mensurative experiments), comparing sites that are more or less impacted or a single site over a time course of disturbance (Miller et al. 1999). As pointed out earlier in this review ( p. 237), the responses of organisms at sites in mensurative experiments may differ not only according to a speciﬁc perturbation but also according to numerous other factors. One of the few manipulative experiments on coral reefs suggests that there may be no effects of enrichment on epilithic algae (Larkum & Koop 1997). In addition, algae may respond to nutrient pulses by fast, short-term uptake rates and subsequent nutrient storage, which may make interpretation of manipulative experiments difﬁcult if algae use stored nutrients for growth when in low nutrient conditions. Some caution is also required with experiments of this type where grazing rates of invertebrates and ﬁshes may increase in response to an increase in algal growth and/or biomass (see also Heck et al. 2000).

Coral reefs Coral reefs are one of the most diverse marine ecosystems but they are currently under direct threat from anthropogenic activities (e.g. eutrophication and increased sedimentation). Growth of reef-building corals is responsible for the framework of coral reef systems that dominate the latitudes between 25°S and 25°N. Associated with reef-building corals are symbiotic dinoﬂagellates (zooxanthellae). Reef-building corals are dominated by biological and physical factors of their environment (Hoegh-Guldberg 1999). Stressors (e.g. nutrient enrichment, turbidity, sedimentation, salinity and temperature extremes) may reduce the growth rate and reproductive capacity of reef-building corals. Extremes may result in high rates of mortality. Fluorescent banding in corals has been used as a natural tracer of freshwater inputs to nearshore environments. Yellow–green ﬂuorescent bands were found in corals growing within 20 km of the shore and were not present in corals from mid- and outer-shelf regions of the Great Barrier Reef (Isdale 1984). The timing, width and intensity of the ﬂuorescent bands 268

FRESHWA TER EFFECTS ON ESTUARI N E A N D C O A S T A L H A B I T A T S

correlated strongly with summer monsoonal rainfall and coastal runoff. Natural periodicities in historic ﬂow have been obtained from ﬂuorescent bands in corals since cores drilled from corals may show growth of corals covering a period of 100 yr or more (Smith et al. 1989). Flow rates decreased 59% between pre- and post-canal eras in Florida Bay (USA) and the periodicity in runoff was lost when ﬂows were diverted by canals (Smith et al. 1989). Fluctuations in salinity limit the distribution of reef-building corals in coastal regions, largely because coral reef environments are typiﬁed by a high degree of stability (HoeghGuldberg 1999). The structure of coral assemblages changed following heavy terrestrial runoff due to mortality associated with lowered salinity (Sakai & Nishihira 1991). Signiﬁcant decreases in coral cover were noted and the structure of assemblages changed because some species were more susceptible to lowered salinity than others (Sakai & Nishihira 1991). Elevated salinities also have an adverse effect on corals (Porter et al. 1999). Coral growth was reduced at intermediate salinities (39) and all corals died at high salinities (44– 53, Nakano et al. 1997). If freshwater ﬂows are reduced so that waters become hypersaline this reduction could be detrimental to reef-building corals. Corals are typically found between 18°C and 36°C, although the optimal temperature range is 22–28°C. Rises in temperature above their upper limits frequently lead to coral bleaching (loss of pigmentation), whereas large falls in temperature have few effects (Wilkinson 1999). Changes in temperature from global climate change are more likely to affect corals than changes in temperature resulting from freshwater input. Corals are often absent in areas close to river mouths because large amounts of sediment enter the sea and reduce the transmission of light through the water column or smother corals. High sediment environments typically have low coral diversity and are dominated by sedimentresistant species. Increased sedimentation is most likely to damage coral reefs adjacent to land masses and on shallow continental shelves (fringing, platform and barrier reefs, Wilkinson 1999). Experimental studies have found that increased amounts of suspended sediment result in reduced growth rates of corals (Rice & Hunter 1992). In addition, burial of corals may result in bleaching and mortality after which the bare coral skeleton can become covered in algae preventing further recruitment of corals (Wesseling et al. 1999). Recruitment may be reduced in high sediment environments because of effects on larval survival and settlement (Gilmour 1999). Therefore, increased sediment discharge from rivers may have signiﬁcant effects on coral reefs. Increased nutrients often favour the growth of algae, which then leads directly to overgrowth of coral (Wilkinson 1999, Koop et al. 2001, see also p. 255 and p. 264). However, evidence suggests that nutrients do not generally lead to phase shifts simply by enhancing algal competitiveness (McCook 1999). Most eutrophic reefs have low levels of herbivores, either directly due to ﬁshing or indirectly due to unsuitable water conditions and low herbivore density may indirectly affect corals since herbivores graze on algae (McCook 1999). The mechanism by which nutrients inﬂuence algal and coral abundances and thus result in reef degradation is clearly more complicated than previously thought. Only one replicated in situ experiment has examined the effects of nutrients on coral reefs (ENCORE – Enrichment of Nutrients on a Coral Reef Experiment conducted at One Tree Lagoon, Great Barrier Reef, Australia; Koop et al. 2001). Increased nutrients resulted in mortality of corals, reduced coral growth, increased susceptibility of corals to breakage and reduced settlement of coral larvae (Koop et al. 2001). Increased nutrients through, for example, increased freshwater input clearly have the potential to affect coral reefs. The effect of other pollutants such as complex organic and heavy metals on coral reefs is largely unknown (Wilkinson 1999). Their effects may be magniﬁed if corals are stressed by other factors (e.g. large ﬂuctuations in salinity). 269

BRON W YN M . GI LLANDERS AND MI C H A E L J. K I N G S F O R D

Effects on other ﬂora and fauna Phytoplankton Productivity in estuarine waters is inﬂuenced by freshwater inﬂow and other factors, including latitude, season, irradiance, temperature, nutrient loading and recycling, grazing, and watershed geomorphology and development (Mallin et al. 1993). Freshwater inﬂow may also inﬂuence some of these factors (e.g. salinity, light, nutrient loading) and therefore factors that control river ﬂow will probably affect primary productivity of estuaries (Mallin et al. 1993, Snow et al. 2000). Many studies have found a positive correlation between phytoplankton biomass and the magnitude of freshwater inﬂow (Malone et al. 1988, Mallin et al. 1993, Harding 1994, but see Snow et al. 2000). This correlation is thought to occur through two processes. First, more stable hydrodynamic conditions are created by vertical stratiﬁcation thus retaining phytoplankton inside estuaries. Second, increases in catchment rainfall and subsequent river ﬂow increase nutrient availability to primary producers in estuaries. Annual cycles of nitrate and salinity in estuaries are also correlated with variations in ﬂow from rivers (Malone et al. 1988, Mallin et al. 1993, Snow et al. 2000). Maximum chlorophyll a (an indicator of biomass of phytoplankton) coincided with or lagged the salinity minima, depending on the year (Malone et al. 1988). In other studies, years of elevated primary production and chlorophyll a were associated with years of increased nitrate and river ﬂow and decreased salinity (Mallin et al. 1993, Harding 1994). The timing of pulse blooms coincided with episodic rainfall and runoff (Mallin et al. 1993). Phytoplankton and benthic microalgal chlorophyll a were highest in ﬂows of 1 m3 s−1 in a South African estuary and decreased as ﬂow increased above this value (Snow et al. 2000). Flow rates of 1 m3 s−1 were sufﬁcient for optimal uptake and use of nutrients. The accumulation of phytoplankton biomass (i.e. a bloom) and the location of blooms depend on appropriate environmental conditions (i.e. conditions where net phytoplankton growth is positive) such as turbidity, nutrient concentrations and grazing pressure. In addition, blooms will also be dependent on mechanisms that may transport phytoplankton while and after it grows (Lucas et al. 1999b). Phytoplankton blooms usually begin when nutrient concentrations exceed limiting levels (Lucas et al. 1999a), such as when pulses of fresh water enter an estuary. Growth rates of phytoplankton may be higher in shallow regions than deep regions of an estuary when the estuary is well mixed, rates of benthic grazing are low and turbidity is high (Lucas et al. 1999a). If turbidity is low and benthic grazing is rapid then growth rates of phytoplankton will generally decrease, as the water column becomes shallower. Factors such as residence time (how long material remains in a region), the rate at which material is transported into or out of a region and whether the estuary has a channel that may act as a conduit may be important in determining blooms (Lucas et al. 1999b). These mechanisms will act over timescales of days or weeks but other transport mechanisms may occur on tidal (or hourly) timescales. The effects of freshwater input on phytoplankton may depend on the amount and seasonality of input (Drinkwater 1986). For example, in a West Australian estuary 90% of river ﬂow occurs during a 3-month period (winter). Immediately following river ﬂow, diatom blooms are typically dominated by three genera (Cerataulina sp., Skeletonema costatum and Chaetoceros sp., Lukatelich & McComb 1986). There are also intense blooms of 270

FRESHWA TER EFFECTS ON ESTUARI N E A N D C O A S T A L H A B I T A T S

Nodularia spumigena in summer; these blooms have a major impact on the level of chlorophyll a concentrations. In years of low-river ﬂow (and reduced nutrient input) there were no signiﬁcant blooms of phytoplankton. Nutrient loading from the rivers stimulated winter and spring diatom blooms, and subsequent recycling of nutrients supported Nodularia blooms in summer (Lukatelich & McComb 1986). Blooms of Nodularia then collapse as salinity increases, although other factors may also play a role. There was a close relationship between the magnitude of the Nodularia bloom, minimum salinities and phosphorus load so that the minimum salinity in winter was used to predict accurately the magnitude of the bloom in the following summer (Lukatelich & McComb 1986). While there is evidence that salinity and the magnitude of the Nodularia bloom are correlated, this relationship may not be as predictive if phosphorus in the form of fertilisers was reduced in catchments. Besides a correlation between phytoplankton biomass and freshwater inﬂow, the structure of assemblages of phytoplankton may also be inﬂuenced by factors affected by freshwater inﬂow, for example, salinity. During high freshwater ﬂows and at the heads of estuaries, freshwater assemblages dominate, whereas at the mouths of estuaries and in more saline waters marine species dominate. Increased freshwater input favoured cryptomonads, whereas under more saline conditions diatoms dominated (Mallin et al. 1993). In a microcosm experiment examining the effects of salinity on assemblages of plankton in coastal lagoons, the dominant taxa varied with salinity (Greenwald & Hurlbert 1993). Cryptomonads increased in abundance with increasing salinity, whereas chlorophytes were most abundant at 51 and pyrrhophytes (dinoﬂagellates) at 0 or 51 (Greenwald & Hurlbert 1993). At intermediate salinities (8.5, 17 and 34), dinoﬂagellates, diatoms and cryptophytes were co-dominants (Greenwald & Hurlbert 1993, see also section on zooplankton, p. 275). Biomass of phytoplankton (chlorophyll) and primary production are generally elevated near riverine plumes (salinity 15–20) and fronts (20–30) compared with open-ocean waters (>30) (Dustan & Pinckney 1989, Grimes & Finucane 1991, Grimes & Kingsford 1996). An exception was the Congo River plume (Democratic Republic of Congo, Central Africa) where phytoplankton biomass and productivity were similar to values in the Atlantic Ocean outside the plume (Cadee 1978). The Congo River plume is relatively narrow resulting in short ﬂushing times of brackish water and short residence times of phytoplankton (see above). The relationship between productivity and plume waters, therefore, is not simple. Production often increases with size of the discharge plume (Grimes & Kingsford 1996). Large rivers such as the Mississippi (eastern North America), Columbia (western North America) and the Changjiang (China) have high productivity in their discharge, whereas smaller river discharges such as the Rhone (western Europe), Ashley and Cooper (eastern North America) and the Burdekin (eastern Australia) have lower productivity.

Invertebrates Freshwater runoff and related changes to water chemistry (salinity, temperature) and sediment load (light) can greatly inﬂuence recruitment, growth, movement, mortality and fecundity of invertebrate populations (Table 6). There have been few experimental studies on the effect of freshwater inﬂow on marine invertebrates (but see Irlandi et al. 1997). 271

Type of study

Effect

272

Ilyanassa obsoleta

Salinity Arenicola cristata

Phyllorhiza punctata

Penaeus merguiensis

Experimental, Size of larvae and behaviour laboratory aquaria, varied with salinity pulse

Experimental, Size of larvae varied with laboratory aquaria, salinity; increased mortality pulse at decreased salinities

Correlative

Collection site of spawning individuals not mentioned Collection site of spawning individuals not mentioned

2–4 salinity treatments, 4 replicates of 400 ml beakers 3–4 salinity treatments, 3 replicates of 400 ml beakers

2 temperature treatments, 200 eggs in 200 ml per treatment 7 sites with 3 depth strata and 2 replicates at each 4 temperature treatments, 2 replicates, settlement plate with 10–20 scyphistomae

Florida, USA 29 N, 85 W (collection site)

Postlarval abundance correlated Queensland, Australia with temperature 17 S, 140 E Experimental, WA, Australia Scyphistomae inactive with laboratory aquaria, retracted tentacles at low 32 S, 115 E press (collection site) temperature (14°C)

4 temperature treatments

Spatial design

Florida, USA 24 N, 81 W (collection site)

Location Lat. & Long.

Richmond & Woodin 1996

Rippengale & Kelly 1995

Staples & Vance 1985

Roller & Stickle 1993

Fitt & Costley 1998

Reference

Monitored 4 × over Richmond & Woodin 1996 14 days

Monitored 3–6 × over 5–12 day period

Sampled twice weekly Oct.–May each year over 4 yr Observed daily for 36 days

Monitored hourly to daily for 4 days (larvae)

Larvae at each temperature for 72 h, monitored 24 h after inducer added

Temporal design

Factors inﬂuencing development and growth of invertebrates. See Table 4 for further details.

Water temperature Cassiopea xamachana Experimental, Ability of larvae to settle and laboratory aquaria, metamorphose changed little for temperatures between 15°C press and 33°C; ability to transfer food to mouth disrupted at temperatures <18°C Lytechinus variegatus Experimental, Developmental rates and laboratory aquaria, percent survival varied directly with salinity press

Factor/Species

Table 6

BRONWYN M. GILLANDERS AND MICHAEL J. KINGSFORD

273

Experimental, Mortality of larvae less at 27 laboratory aquaria, and 33 than lower salinities press

Experimental, Developmental rates and laboratory aquaria, percent survival varied directly press with salinity

Experimental, ﬁberglass tanks

Echinometra lucunter

Lytechinus variegatus

Zooplankton

Correlative

Correlative

Starﬁsh, molluscs, lobster, polychaetes

Penaeus merguiensis Postlarval abundance correlated with salinity

Mortality where freshwater salinities found

Experimental, Preference for higher salinity; laboratory aquaria, sensed decreased salinity by press moving

Lobsters

Rhopilema esculentum Experimental, laboratory, press

Experimental, All survived exposure to laboratory aquaria, freshwater pulse

Lithopoma tectum

Total zooplankton abundance decreased with salinity for salinities >17 No survival at low salinities

Experimental, 100% mortality when salinity laboratory aquaria, dropped from 36 to 2; all pulse survived 50% drop in salinity

Lytechinus variegatus

Gulf of St Lawrence, Canada 47 N, 62 W Queensland, Australia 17 S, 140 E

California, USA 32 N, 117 W (collection site) Liaoning, China 41 N, 123 E (collection site) NH, USA 43 N, 71 W (collection site)

Florida, USA 29 N, 85 W (collection site)

Florida, USA 27 N, 80 W (collection site)

Florida, USA 26 N, 80 W (collection site)

Florida, USA 26 N, 80 W (collection site)

7 sites with 3 depth strata and 2 replicates at each

2 salinity treatments (preference experiment) or range of salinity (avoidance experiment) SCUBA surveys of general area

Lu et al. 1989

Greenwald & Hurlbert 1993

Roller & Stickle 1993

Metaxas 1998

Irlandi et al. 1997

Irlandi et al. 1997

Thomas & White 1969 Sampled twice Staples & weekly Oct.–May Vance 1985 each year over 4 yr

Sampled after upestuary gale

Experiment run for Jury et al. 6 h (preference) or 1994 60 min (avoidance)

50-min pulse treatment, examined after 24 h 50-min pulse 2 treatments, 7–8 treatment, replicates, each in examined after 18.9 L bucket 24 h Monitored after 6 salinities, 3 24 h, repeated replicates/salinity (150 ml SW in each for 4 development stages bowl) Monitored hourly 7 salinity to daily for 4 days treatments, 200 eggs in 200 ml (larvae) per treatment Monitored 6× over 5 salinities, 4 114 days replicates of 380 L tanks 5 salinity treatments Monitored every 2 days for a month

2 treatments, 7–8 replicates, each in 18.9 L bucket

FRESHWATER EFFECTS ON ESTUARINE AND COASTAL HABITATS

274

Effect

Fiordland, NZ 45 S, 167 E

WA, Australia 32 S, 115 E (collection site)

Location Lat. & Long.

continued

Postlarval abundance correlated with nutrients

No relationship between ﬂow rate and growth

Growth rate declined with increasing ﬂow

Growth rate declined with increasing ﬂow

Queensland, Australia 17 S, 140 E

Washington, USA 48 N, 123 W (collection site) Washington, USA 48 N, 123 W (collection site) Washington, USA 48 N, 123 W (collection site)

NY, USA 43 N, 75 W

Brachiopods, antipatharians, Fiordland, NZ gorgonians and sepulid 44–46 S, polychaetes at shallower depths 166–167 E than normal due to low light

Decreased abundance of invertebrate predators and predation intensity at lowsalinity layer compared with depths below it

Experimental, ﬁeld Increased abundance of some species with increased ﬂow study, press

Correlative, ﬁeld study

Correlative and experimental ﬁeld study, press

Scyphistomae failed to survive Experimental, laboratory aquaria, at low salinity (5) press

Type of study

Experimental, laboratory aquaria, press Pseudochitinopoma Experimental, occidentalis laboratory aquaria, press Balanus glandula, Experimental, Semibalanus cariosus, laboratory aquaria, Pollicipes polymerus press Nutrients Penaeus merguiensis Correlative

Water motion Fouling assemblage (barnacle and hydroid) Membranipora membranacea

Water clarity (light) Invertebrates

Mytilus edulis galloprovincialis

Phyllorhiza punctata

Factor/Species

Table 6

7 sites with 3 depth strata and 2 replicates at each

5 treatments, 2 replicates

5 treatments, 2 replicates

3 treatments, 4 replicates/ﬂow treatment 5 treatments, 2 replicates

6 salinity treatments, 2 replicates, settlement plate with 10–20 scyphistomae 2 sites, 4–5 depths, 3–4 transects/depth or 10 replicate Mussels (experiment) 18 SCUBA dives at 15 localities; 1 transect per dive from surface to 10–40 m

Spatial design Rippengale & Kelly 1995

Reference

Grange et al. 1981

Sampled twice weekly Oct.–May each year over 4 yr

Monitored growth over 6 months

Monitored growth over 6 months

Monitored growth over 6 months

Staples & Vance 1985

Eckman & Duggins 1993

Eckman & Duggins 1993

Eckman & Duggins 1993

Monitored after 4– Judge & 6 wk and 7 months Craig 1997

One-off sampling

Repeated 3× over Witman & 10 months Grange 1998 (correlative study); experiment run twice over 4 days

Observed daily for 36 days

Temporal design

BRONWYN M. GILLANDERS AND MICHAEL J. KINGSFORD

FRESHWA TER EFFECTS ON ESTUARI N E A N D C O A S T A L H A B I T A T S

Zooplankton The distribution, abundance, and diversity of zooplankton within estuarine and marine waters is inﬂuenced by ﬂuctuating patterns of salinity, temperature and turbidity, as well as by tidal and river ﬂows (Roper et al. 1983, Laprise & Dodson 1994). These environmental variables vary horizontally (e.g. along the estuary) and vertically (e.g. depth) and the zooplankton assemblages probably reﬂect this spatial variability. Freshwater discharge affected the zooplankton assemblage in terms of both horizontal and vertical distributions in an experimental study examining freshwater discharge from a hydroelectric power plant to a fjord (Kaartvedt & Nordby 1992). In addition, levels of freshwater input inﬂuence environmental variables and if the extent of freshwater input varies from month to month then the assemblages are also likely to vary (Nyan Taw & Ritz 1978). Zooplankton within estuarine waters typically represent freshwater, estuarine and marine environments (Nyan Taw & Ritz 1978, Roper et al. 1983, Laprise & Dodson 1994, Dauvin et al. 1998). Marine or coastal species are probably carried into estuaries with the tide, whereas freshwater species are carried down the river (Roper et al. 1983, Dauvin et al. 1998). Assemblages are characterised by salinity, although temperature may be correlated with salinity (Laprise & Dodson 1994). Species diversity has also been found to vary as a function of salinity. Lowest diversity was found in regions or seasons with greatest freshwater inﬂuence (Kidd & Sander 1979, Laprise & Dodson 1994).

Meiofauna Meiofaunal abundance and composition are likely to be determined by a variety of physical factors including particle size of the sediments, detrital load, temperature and salinity (Coull 1999). Estuarine sediments typically contain 106 m−2 of meiofauna, with nematodes (constituting 60–90% of total fauna) and copepods (10–40%) being the most abundant taxa (Coull 1999). In many places, meiofauna play a critical role in estuarine functioning (e.g. enhancers of biomineralisation, food for higher trophic groups) and, therefore, caution should be used to ensure that habitats of meiofauna (e.g. mudﬂats) are not eliminated by any alterations to freshwater input. In addition, meiofauna are consumed by juvenile ﬁshes and changes to meiofauna within estuaries may, therefore, also affect other organisms. Assemblages of meiofauna differ between mud and sand and any changes in ﬂow regime that alter sedimentology will have effects on abundance and composition of meiofauna. There is a marked effect of salinity on meiofauna, with decreased abundance and number of species as one moves from the sea to freshwater habitats (reviewed by Coull 1988). Episodic salinity changes such as those associated with storms, will affect organisms in the top 2–3 cm of natural sediment containing both meio- and macrofauna (Coull 1999).

Macrofauna and other invertebrates Polychaetes, molluscs and crustaceans dominate the benthic macrofauna in marine portions of rivers and estuaries (Hutchings 1999). There is little information on the functioning of estuarine systems and how these systems respond to ﬂoods (Hutchings 1999). The effects of variability in runoff on macrofauna are poorly known. Most of the data are from correlations between ﬁshery estimates and runoff and are from studies of organisms found in soft-substrata of sheltered estuarine environments (Table 7). The results do not provide 275

276

Monthly mean

Monthly pulses

Berre Lagoon

Koenigshafen Bay

Polychaetes

Benthic assemblage

Benthic assemblage Benthic assemblage

Daily Monthly mean

Loxahatchee

Apalachicola

Hawkesbury, Australia

Hard-shell clam

Shrimps

Shrimp Shrimp Shrimp Blue crab Blue crab Blue crab Oyster Fouling assemblage

Interannual

Lobster Halibut Haddock Soft-shell clam Lobster Lobster Halibut Cod Cod

Species

Monthly mean (summer) Annual mean Monthly mean Monthly mean Annual mean Monthly mean Monthly mean Annual mean Seasonal

Zambezi Sheltered estuarine environments Southampton Water

Region III, Norway North Norwegian shelf

June April March Summer Interannual

Annual mean

Coastal and large estuarine environments St Lawrence, USA

Miramichi, USA St Lawrence, USA

Runoff data

Recruitment, negative relationship Catch Density Biomass Catch Density Biomass Catch Growth, density, negative relationship Abundance Richness of species, negative relationship Community structure, relationship changed Abundance

Catch (lag 6 yr) Catch (lag 10 yr) Catch (lag 8 yr) Catch (lag 5 yr) Larval stage I Catch (lag 9 yr) Catch (lag 10 yr) Survival index (lag 1 yr) Year class strength and catch (lag 1 yr and 3 yr) Catch rate

Variable

Schiedek & Schöttler 1991

Stora & Arnoux 1988

Nichols 1985 Jones 1987

McPherson et al. 1984

Meeter et al. 1979

Mitchell 1976

Gammelsrod 1992

Skreslet 1976 Skreslet 1997

Sutcliffe 1973

Sutcliffe 1972

Reference

Runoff effects on pelagic and benthic macrofauna (adapted from Ardisson & Bourget 1997). Correlations are positive unless mentioned.

River

Table 7

BRONWYN M. GILLANDERS AND MICHAEL J. KINGSFORD

FRESHWA TER EFFECTS ON ESTUARI N E A N D C O A S T A L H A B I T A T S

unambiguous links between freshwater runoff and benthic populations because they were not consistent for different species and spatial scales (Ardisson & Bourget 1997). In addition, the patterns often do not hold through time (see also Drinkwater & Myers 1987). Recently, no relationship was found between indices of runoff (namely, cumulative spring runoff, spring peak runoff and mean annual runoff ) and benthic organisms (variables: biomass, abundance and mean weight of epibenthic species) at different spatial scales (Ardisson & Bourget 1997). This study is one of the few experimental investigations that looked for such relationships. Navigation buoys that were a few kilometres from the source of runoff were sampled after a 6-month immersion period. Therefore, the spatial and temporal scales were close to the response variables (Ardisson & Bourget 1997). Correlations appeared slightly stronger at small spatial scales (Ardisson & Bourget 1997). Increased freshwater ﬂows have been linked to high abundances of some invertebrates. Increased nutrients purportedly stimulate primary and secondary production resulting in increased catches of estuarine and coastal ﬁsheries (Loneragan & Bunn 1999). A secondary effect of rainfall, such as reduction in salinity, or increased discharge and turbulence, may stimulate movement of juvenile banana prawns (Penaeus merguiensis) from their nursery habitats (or they may simply be washed out to sea by high ﬂows) resulting in a high positive correlation between catches of prawns and rainfall (e.g. in the southeastern Gulf of Carpentaria, Australia: Vance et al. 1985, 1996, Loneragan & Bunn 1999). The strength of the correlation between rainfall and offshore prawn catches was thought to be dependent on the size of the river catchment; heavy rainfall may have an immediate impact on nursery habitats of estuarine prawns but in small catchments oceanic water quickly alters salinity (Vance et al. 1998). Heavy rainfall in larger catchments causes a much larger volume of fresh water to ﬂow through estuarine systems and over a much longer time, thus heavy rain has a much greater effect on nursery habitats (Vance et al. 1998). Other species of prawns have also shown positive correlations with rainfall and river ﬂow. Examples are school prawns (Metapenaeus macleayi) in the Hunter River (Ruello 1973) and Clarence River, NSW, Australia (Glaister 1978), pink shrimp (Penaeus duorarum) in the Florida everglades (Browder 1985), bay prawns (Metapenaeus bennettae), king prawns (Penaeus plebejus) and tiger prawns in the Logan River, Queensland, Australia during summer (Loneragan & Bunn 1999) and shrimp (P. indicus) in the Zambezi (Jorge da Silva 1986). Mud crabs show similar correlations (Loneragan & Bunn 1999). These studies did not always ﬁnd a relationship between rainfall and total production for all regions (e.g. Glaister 1978, Vance et al. 1985, 1998). Glaister (1978), for example, found a relationship only if the estuarine and oceanic regions were considered separately. Likewise, Vance et al. (1985) did not observe a strong positive relationship between rainfall and subsequent offshore commercial catch of adults for all regions of the ﬁshery. There is clearly a complex relationship between rainfall in an area and subsequent freshwater runoff and river discharge that makes interpretations of correlations between rainfall and production difﬁcult (Glaister 1978). Despite this difﬁculty, river discharge appears to be important for production of penaeids. Therefore, any restriction of freshwater ﬂow may have a detrimental effect on the ﬁshery. Correlations have also been found between landings of lobsters and freshwater ﬂow, suggesting that recruitment to the lobster ﬁshery may be controlled by variability in freshwater input (Sutcliffe 1973). More recently, the relationship between landings of lobsters and runoff has been demonstrated to be predictive (Fig. 7 and Sinclair et al. 1986). Inter-annual variability in runoff has an impact on ﬁsheries production but the mechanism 277

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Figure 7 Comparison of observed Quebec lobster catch in the Gulf of St Lawrence and “predicted” landings from a regression involving RIVSUM (i.e. combined runoff from the St Lawrence, Ottawa and Saguenay rivers). The solid line represents reported landings; the broken line the “predicted” landings. The crosses representing the lobster catch from 1980 to 1984 have been added. From Sinclair et al. (1986), published with permission.

by which the variability is induced is not well understood (Sinclair et al. 1986). Three different hypotheses have been proposed as to how river plumes and associated runoff may inﬂuence recruitment, namely the short-food-chain hypothesis, the total-larval-production hypothesis and the physical retention hypothesis or some combination of these (Grimes & Kingsford 1996). The short-food-chain hypothesis states that recruitment will be enhanced in the vicinity of river plumes because organisms there experience superior feeding conditions, grow faster and survive better (Grimes & Finucane 1991). The total-larval-production hypothesis states that trophic conditions support such high total production that negative effects of unfavourable dynamics are overridden. The third hypothesis suggests that plumes and associated circulations facilitate the retention of organisms within an area (Grimes & Kingsford 1996). Riverine input may also inﬂuence the biomass of jellyﬁshes. Variation in the stock and catch of Rhopilema esculentum from Liaodong Bay (China) was thought to be due to variation in riverine input because the abundance of medusae was positively correlated with the input of fresh water from rivers (Lu et al. 1989). However, excessive runoff coincided with a decline in the abundance of medusae. Freshwater runoff also affected the patterns of abundance of medusae among estuaries separated by tens of kilometres (Rippingale & Kelly 1995). In contrast to the above examples, landings of oysters in the Apalachicola estuary (Florida, USA) are negatively correlated with river ﬂow. The highest landings coincided with drought conditions (Meeter et al. 1979, Wilber 1992). High oyster production during low ﬂow years could be due to increased primary productivity that contributes to increased growth rates but with continued low ﬂows production would decrease as nutrient limitation sets in (Livingston 278

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et al. 1997). Permanent reductions in river ﬂow, whether through naturally occurring droughts or upstream water use, could end the productive oyster industry in the Apalachicola estuary (Livingston et al. 1997, 2000). Oyster populations may decline because of increased disease (Perkinsus marinus) as salinities increase further up the estuary. The mole crab (Emerita brasiliensis) increased in abundance with distance from freshwater discharge from a man-made canal (Lercari & Defeo 1999). Crabs were smaller with reduced fecundity near the freshwater discharge probably because large crabs had higher mortality rates and there was a failure of recruitment (Lercari & Defeo 1999). The generality of these results are unknown, as the study focused on one area (22 km of sand beach) with two freshwater discharges. Rainstorms cause dramatic changes to estuaries due to riverine input and terrestrial freshwater runoff. There are heavy mortalities of benthic invertebrates after rainfall from large amounts of freshwater input (e.g. starﬁsh, molluscs, lobsters, polychaetes: Thomas & White 1969, sea urchins: Andrew 1991). Such mortalities are typically attributed to limited osmoregulatory capabilities of organisms that are stenohaline (Roller & Stickle 1993, Jury et al. 1994). In an experimental laboratory study, lobsters were able to sense and avoid areas of reduced salinity by moving (Roller & Stickle 1993, Jury et al. 1994). One experimental study mimicked the effect of freshwater ﬂow from canal discharge and examined survivorship and feeding behaviour of a gastropod and an echinoderm (Irlandi et al. 1997). In the ﬁrst experiment, salinity in the experimental treatments decreased from 36 to around 2 over 30 min and then after 20 min at low salinity, the salinity rapidly increased back to ambient levels. The control treatments remained at 36–37 throughout the experiment. After 24 h, all the sea urchins (Lytechinus variegatus) exposed to the low salinity had died while all control sea urchins were alive. In a second experiment, all sea urchins survived a 50% drop in salinity (Irlandi et al. 1997). A similar experiment to the ﬁrst experiment on sea urchins found that all snails (Lithopoma tectum) survived exposure to freshwater. In experiments involving freshwater pulsing, the control group of sea urchins consumed more algae than the experimental group but the reverse pattern was found for snails (Irlandi et al. 1997). The effect of freshwater on gastropods and urchins may be different and the species distribution and grazing rates may be altered with possible cascading effects on ecosystem functioning of seagrass beds. The developmental patterns and rates of mortality of many marine invertebrate larvae are inﬂuenced by variations in salinity (see references in Roller & Stickle 1993 and Richmond & Woodin 1996). For example, larvae grown in reduced salinities grow at slower rates. In an experimental study, in which salinity was altered to simulate storms, the severity (degree to which salinity was lowered), duration and timing of the storm relative to larval age all determined the larval response of a polychaete Arenicola cristata and a gastropod Ilyanassa obsoteta (Richmond & Woodin 1996). Transport of invertebrate larvae into estuaries probably depends on the vertical positioning of larvae in response to vertical stratiﬁcation in salinity (Hughes 1969, see below). The occurrence of stratiﬁed ﬂows in coastal systems, many of which are driven by riverine output, are thought to be of fundamental importance for the transport of invertebrate (and vertebrate) larvae (Norcross & Shaw 1984). Larvae often undergo vertical migration to facilitate inshore or offshore transport (selective tidal stream transport; Forward & Tankersley 2001) and the nature of migration often changes with developmental form. The effects of major alterations to mean ﬂow, as well as the magnitude and duration of pulse events, on larval transport and retention is unknown. 279

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Benthic and pelagic stages of jellyﬁshes are also vulnerable to changes in salinity associated with freshwater input (Kingsford et al. 2000). Salinity may affect growth, asexual reproduction, strobilation and mortality rates of polypoid forms, as well as mortality rates of medusae (Kingsford et al. 2000). Highest rates of strobilation were found at medium salinities for Chrysaora quiquecirrha, although there was an interaction with temperature so that strobilation increased with temperature at salinities >20 (Purcell et al. 1999). Besides altering salinity, freshwater may also alter ﬂow conditions especially after ﬂoods. There has been limited research on how turbulence may affect individuals or assemblages and most of the research has been done in streams or rivers (e.g. Matthaei et al. 1997, Robson et al. 1999). The data suggest, however, that invertebrate movement and dispersal, food supply (for suspension and deposit feeders), and gas and nutrient exchange may all be modiﬁed by ﬂow. Sediment deposition may have a variety of effects on benthic organisms ranging from interference with feeding through to death by smothering (Ryan 1991). However, it is often difﬁcult to separate the effects of sediment from that of pollution. Moderate rates of sediment deposition, particularly of ﬁne sediment, often have adverse effects on infaunal invertebrates but effects may be less on organisms that can move (see also p. 275). Rather than a total loss of fauna, the structure of assemblages may change. Contaminants carried by rivers may induce changes in populations and assemblages of biota resulting in loss of diversity. Nereids, capitelids and spionid polychaetes were signiﬁcantly more abundant in polluted than unpolluted sediments, whereas a range of taxa (e.g. crustaceans and gastropods) showed the reverse pattern (Stark 1998). Contaminants may disturb sensitive taxa and create conditions amenable to opportunistic, less sensitive taxa. Annelids were ranked as the taxon most sensitive to heavy metals followed by crustaceans and then molluscs in a review of the effects of metals on estuarine invertebrates (McLusky et al. 1986). There are few experimental ﬁeld studies that investigate the effect of contaminants such as heavy metals on invertebrate assemblages, especially in soft-sediment habitats. In one such study, there were reduced numbers of organisms in copper-treated sediments relative to the controls (Morrisey et al. 1996). The effects may vary over time because the toxicity of metals changes in response to salinity and temperature and will therefore be inﬂuenced by freshwater inputs.

Fishes Species that spend most or all of their life in freshwater The migration of ﬁshes and other organisms between the sea, estuary and rivers may be inhibited or prevented by barriers on rivers. It was estimated that of aquatic habitat that is potentially available to ﬁshes, artiﬁcial barriers have obstructed between 32% and 49% of waterways in southeastern Australia (Harris 1984). In addition, the timing of spawning in some species may relate to water ﬂow and ﬂood conditions. Therefore, there is a risk to ﬁshes if ﬂows are manipulated and ﬂoods dampened through reservoirs. Many of the native species of freshwater ﬁshes within Australia (at least 26 species) undergo some form of migration (Reynolds 1983, Harris 1984). However, there is little evidence for movements of freshwater ﬁshes in excess of 10s of kilometres, although the smaller species have been largely unstudied (Humphries et al. 1999). During ﬂooding, 280

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movements of ﬁshes may occur but there is little direct evidence available and rivers are difﬁcult to monitor under ﬂood conditions (Harris 1984). Impoundment of streams with dams, weirs and other physical barriers may affect the distribution and abundance of ﬁshes but the effects vary greatly (Harris 1984). Larvae and small juveniles of catadromous species (spend most of their life in fresh water and migrate to the sea to breed) will migrate upstream, until they reach major obstructions such as tidal barriers and dams (e.g. Anguillidae, Mugilidae: Harris 1984). Fishes may be able to move upstream during periods of low or normal ﬂow provided that a ﬁsh ladder or ﬁshway is present (Cadwallader & Lawrence 1990). Although low-level weirs may be only partial barriers, larger weirs and high level dams may completely block migration. The stimulus for spawning, recruitment and migration in many freshwater ﬁshes, as well as salmonids, is thought to be ﬂooding or rises in ﬂow (see references in Harris 1984). Some species will spawn only when there is a suitable ﬂood, whereas others will spawn seasonally, but more intensely when there are ﬂoods (Walker & Thoms 1993). Since impoundments suppress downstream ﬂooding, they may have signiﬁcant effects on the ecology and reproductive biology of freshwater ﬁshes (Harris 1984). Spawning and recruitment may also depend on changes in water temperature and salinity gradients so that the rate of change and volumes of freshwater ﬂows may impact on the population dynamics (Bourman & Barnett 1995). Recently, Humphries et al. (1999) have suggested that many species may spawn independently of high ﬂows and ﬂooding and can therefore successfully recruit during periods of low ﬂow. The “ﬂood recruitment model” (Harris & Gehrke 1994), in which ﬂow appears to initiate spawning and for which the ﬂood plain may be an important habitat for larvae, is therefore thought to be inadequate for many species of ﬁshes, including those found in the Murray–Darling system. However, the ﬂood-recruitment paradigm prevails in many parts of the world (Lowe-McConnell 1987). The population density and species’ distribution of Australian bass (Macquaria novemaculeata) has declined since European settlement, and these changes have generally been attributed to obstruction of migration by impoundments on rivers (Harris 1988). The species is catadromous and the sexes are partially segregated. Most males are found in estuarine or lowland habitats, whereas females are found in lagoon or upland lotic habitats. Impoundment and changes in ﬂow may differentially affect the sexes. A correlation between initial year class strength and river ﬂow in both the Hawkesbury and Colo Rivers (NSW, Australia) during the spawning months was found (Harris 1988). This correlation is not surprising given that reproduction in bass is thought to be dependent on ﬂooding (Harris 1986). Conditioned mature females may display a variable response to ﬂow so that a progressively greater proportion of the population is stimulated to spawn by increasing river discharge (Harris 1988). If the frequency and amplitude of ﬂoods are changed through diversion of river ﬂow and construction of impoundments then reproductive success of bass and subsequent recruitment are also likely to be affected (Harris 1984, 1986, 1988). Bass also recruits to sheltered beds of macrophytes (Harris 1988). Changes to these habitats caused by siltation or increased turbidity with, say, an increase in freshwater ﬂow may, therefore, also negatively affect the species. Australian grayling (Prototroctes maraena) are diadromous and dams without ﬁsh ladders will stop their upstream migration (Bishop & Bell 1978). Some species may also be found in estuaries after heavy freshwater discharge when salinities may decline to low levels. The introduced mosquitoﬁsh (Gambusia holbrooki), a pseudomugilid, a scorpaenid and three species of eleotrids were found in estuaries within NSW (Australia: Pollard 1994). 281

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Species spending all or some of their life in estuarine waters Some species may use estuaries as a migration pathway, from fresh water to the sea or vice versa, whereas others spend part of their life cycle within estuaries (e.g. juveniles using estuaries as a nursery habitat). Only a few species of ﬁshes spend their entire lives in estuaries (e.g. some members of the families Atherinidae, Gobiidae and Syngnathidae: Potter et al. 1990, Gill & Potter 1993, Pollard 1994). The factors that make estuaries suitable nursery habitats (e.g. increased food, higher temperatures, turbid waters, lower salinities) may be greatly inﬂuenced by alterations in freshwater input. In particular, changes to salinity will inﬂuence not only the distribution and abundance of ﬁshes but also their habitats (see p. 254). Juveniles of marine ﬁshes are the most abundant group of ﬁshes found in estuaries. Because the juveniles of marine ﬁshes may also be abundant in sheltered, inshore marine waters the term “estuarine-opportunist” has been given to this group of ﬁshes (Lenanton & Potter 1987). Several marine species occur irregularly and in low numbers in estuaries and have been referred to as “marine stragglers” (Lenanton & Potter 1987). Marine stragglers are typically found only in the lower estuary, although they may sometimes penetrate the middle estuary, especially at times of low riverine input (Loneragan et al. 1989, Loneragan & Potter 1990). A model has been developed to determine the effect of reduced freshwater ﬂow on recruitment of ﬁshes in South African estuaries. In the Great Brak estuary where it was tested, annual runoff could be halved from natural conditions with no discernible effects on recruitment of juvenile ﬁshes but thereafter there was a sharp decline in immigration (Quinn et al. 1999). Results of the simulations suggested that an adequate base ﬂow was required but also that the timing of ﬂood releases was critical (Quinn et al. 1999). A number of factors, affected by freshwater input (e.g. river ﬂow, salinity, turbidity) inﬂuence the diversity of assemblages of ﬁshes. The spawning location, and nature of eggs (e.g. benthic or pelagic) and larvae may be important in some systems. Many studies have suggested that salinity, temperature, turbidity and current speed (inﬂuenced by tidal range and freshwater input) are important determinants of species composition and structure of assemblages, although many of these factors are correlated. Reduced or altered freshwater inﬂow has been attributed to reduced ﬁsheries production in many river-dominated estuaries (Livingston 1997, Livingston et al. 1997). Although many studies ﬁnd a relationship between freshwater input and enhanced estuarine production (see also p. 271 and Table 7), the nature of the interaction remains poorly understood (Livingston et al. 1997). Predictions of landings based on environmental variables are generally more accurate for invertebrate stocks than ﬁsh stocks (Drinkwater & Myers 1987). Freshwater input from rivers inﬂuences trophic conditions because nutrient outwelling from estuarine areas contributes to increased primary production, although a variety of factors (e.g. geomorphology of the drainage basin, magnitudes of tidal input) also inﬂuence nutrients and primary production. Natural estuarine productivity will require some freshwater input, but just how much is unknown (Livingston et al. 1997). Assemblages of ﬁshes in estuarine systems may be affected by river ﬂow (e.g. Livingston et al. 1997), largely through its inﬂuence on salinity. Salinity is positively correlated with the number of species, density and biomass of ﬁshes, but may also be correlated with temperature and distance from the estuary mouth (Gunter 1961, Loneragan et al. 1986, Loneragan & Potter 1990, Whitﬁeld 1994). Univariate correlations suggested that the number of species 282

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and density of marine stragglers was positively correlated with salinity and negatively correlated with distance from the estuary mouth (Loneragan & Potter 1990). Marine opportunists were similarly correlated with salinity and distance from the estuary mouth but also positively correlated with temperature. In contrast, the density of the estuarine species was correlated neither with salinity nor with distance from the estuary mouth (Loneragan & Potter 1990). Reduced inﬂuence of salinity on estuarine species could reﬂect the wide salinity tolerance of this group of ﬁshes (Loneragan & Potter 1990). Salinity is inversely related to freshwater discharge and declines in catches in rivers during wet periods may also be related to physical effects of ﬂushing (Loneragan et al. 1986). Assemblages of ﬁshes in seagrasses have also been found to vary with distance from the estuary mouth even though there was essentially no salinity gradient (Bell et al. 1988). Bell et al. (1988) used artiﬁcial seagrass units so that differences were not due to variation in physical complexity of habitats. A decline in species richness and a change in species composition with distance have also been noted in many estuarine systems (Gunter 1961, Blaber et al. 1989, Yoklavich et al. 1991). The distribution and abundance of ﬁshes in estuaries is inﬂuenced by salinity, especially where there is a signiﬁcant freshwater inﬂux (Marais 1988, Loneragan & Potter 1990, Whitﬁeld 1994, Schlacher & Wooldridge 1996, Sheaves 1996), but is not important where there are small and intermittent inﬂows of freshwater (Jones et al. 1996). There are groups of ﬁshes in Australian estuaries that prefer low salinities and are abundant in the upper reaches (e.g. Gobiidae (Pseudogobius olorum, Afurcagobius suppositus, Papillogobius punctatus), Atherinidae (Leptatherina wallacei), Sparidae (Acanthopagrus butcheri) and Teraponidae (Amniataba caudavittata); Gill & Potter 1993, Young et al. 1997, Potter & Hyndes 1999). Others prefer high salinities and are found predominantly in the lower and/or middle reaches of the estuary (e.g. Atherinidae (Leptatherina presbyteroides), Gobiidae (Favonigobius lateralis), Plotosidae (Cnidoglanis macrocephalus) and Apogonidae (Apogon rueppellii); Gill & Potter 1993, Potter & Hyndes 1999). Marine species typically dominate the lower regions of estuaries but become less dominant with increasing distance away from the estuary mouth (e.g. Loneragan et al. 1986, 1989, Bell et al. 1988, Thiel et al. 1995, Potter & Hyndes 1999). The proportion of estuarine opportunists in terms of numbers was 95% in the lower estuary, but only 6% in the upper estuary (Swan estuary, WA, Australia: Loneragan et al. 1989). Similar patterns have also been found in southern Africa (Potter et al. 1990). Some Gobiidae tolerate a wide range of salinities (1 to 35) and remain in the upper region of estuaries even when freshwater discharge is high suggesting that they are euryhaline (Gill & Potter 1993). However, others are better adapted to reduced salinities. For example, Pseudogobius olorum did not survive direct transfer to virtually full strength sea water (Gill & Potter 1993). The effect of salinity on marine species is likely to depend on their salinity tolerance and physiology, such that stenohaline species may not penetrate far into an estuary or survive within it, whereas euryhaline species will be found within estuaries even when salinities are low (Young et al. 1997). For species that complete their life cycles in estuaries (e.g. some members of the Atherinidae, Gobiidae), their success may be due to the hydrological stability of estuaries around the time of spawning (Potter & Hyndes 1999), although different species spawn at different times. Thus, any impact on the amount of freshwater entering estuaries around the time of spawning that radically alters salinity and oceanography throughout the estuary may inﬂuence the distribution and survival of some species of ﬁshes. Timing of recruitment for 283

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transient species coincides with the period of maximum river discharge (Rogers et al. 1984), although times of extremely high ﬂow may limit the amount of useable nursery habitat due to changes in salinity and/or turbidity (Potter et al. 1991, Loneragan & Bunn 1999). If species recruit to nurseries after times of high rainfall and ﬂow (in areas with large catchments) then there may be a physical barrier to recruitment (Loneragan & Bunn 1999). Despite this barrier, estuarine habitats are often thought to be important nursery habitats because of increased abundance of food that results in faster rates of growth compared with other habitats. At times of high rainfall and ﬂow, there may also be increased productivity within estuaries. As the coastal canal system of Florida (USA) moved from an estuary to a freshwaterpulsed lagoon it contributed to the elimination of estuarine-dependent species, an increase in stress-tolerant species and a reduction in habitat for reef-associated ﬁshes that may be vulnerable to changes in salinity (Serafy et al. 1997). Discharge of freshwater from canals into adjacent bays led to wide salinity ﬂuctuations over relatively short periods of time (e.g. hours), with fewer species of ﬁshes near freshwater discharges (Serafy et al. 1997). Species that were in low numbers near the canals were intolerant to freshwater exposure (Serafy et al. 1997). A single, canal discharge killed ﬁshes (Serafy et al. 1997). There are few, if any, studies that examine the extent to which abrupt changes in salinity may affect reproduction, development, feeding and growth in ﬁshes. Some sciaenids have been affected by changes to freshwater input. Argyrosomus hololepidotus, a sciaenid of recreational and commercial importance, was once abundant in South African estuaries that naturally had relatively large freshwater inputs and that were turbid, rather than in more saline estuaries (Marais 1988). Similarly, the decline of the totoaba (Totoaba macdonaldi) in the Gulf of California, and its subsequent placement on the endangered species list is due in part to the diversion of the Colorado River. Former brackish water habitat became hypersaline drastically altering the nursery grounds of the totoaba (Barrera Guevara 1990, see also p. 245). Species composition and assemblage structure of some estuaries may be inﬂuenced by current ﬂow, which largely results from tidal range and freshwater inﬂow (Blaber et al. 1995). In addition, turbidity may be important. Turbidity was positively correlated with ﬁsh abundance (Marais 1988) and detailed studies on juvenile ﬁshes demonstrated that turbidity had a major effect on their distribution (Cyrus & Blaber 1987). Indirect effects of turbidity on ﬁshes may result from excessive siltation, which reduces light and smothers submerged vegetation and associated invertebrate prey (Plumstead 1990). Silt may also have direct effects on ﬁshes by clogging gill rakers and gill ﬁlaments (Bruton 1985). Heavy rainfall following a prolonged dry period can kill many ﬁshes and other gilled organisms in some coastal tributaries and estuaries (Brown et al. 1983). These kills are attributed to stream acidiﬁcation, caused by drainage from oxidised sulphidic sediments (Sammut et al. 1996), although hypoxia may also be important. There may also be sub-lethal effects such as ﬁsh diseases, including a cutaneous fungal infection (Sammut et al. 1995). Species of ﬁsh are affected differently. Estuarine acidiﬁcation may disturb reproduction and recruitment, destroy food resources, reduce species diversity, provide acid-barriers to migration and result in habitat degradation over the long term (Sammut et al. 1995). With increased freshwater input, deeper waters may be depleted of oxygen (hypoxia). Motile, demersal organisms may be able to move from these areas depending on their size, but in some cases dead ﬁshes have been found lying on the bottom (e.g. Rabalais et al. 1998). Hypoxia inﬂuenced mortality, size structure of the population, reproductive behaviour 284

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and spatial distribution of a benthic ﬁsh Gobiosoma bosc (Breitburg 1992). Recruits were more susceptible to hypoxia than older individuals, although adult males continued to guard eggs and shelters until dissolved oxygen approached lethal levels (Breitburg 1992). Hypoxia may therefore have different effects on species, size and sex of organisms. Various chemicals may have an adverse effect on aquatic organisms (see also p. 246). For example, marine organisms can accumulate cadmium. As yet it has no known metabolic role and is considered a potential hazard to crustaceans (Bambang et al. 1994). Many studies of toxicity have been done on adult organisms and there are few data available about toxicity at different developmental stages (but see next section and Bambang et al. 1994, Kingsford & Gray 1996).

Species in discharge plumes and fronts The frontal regions of estuarine plumes are sites of intense biological activity. Densities of phytoplankton, zooplankton, larval ﬁshes and nekton are often high in these regions and are most conspicuous where there is signiﬁcant freshwater input to estuaries (Grimes & Kingsford 1996). River discharge is typically considered to increase the nutrients in the water and result in stimulation of primary and secondary production, which then increases the food available for larvae. Increased food is then thought to lead to faster growth, decreased predation and increased survival of larvae. Larval ﬁshes feed more successfully (i.e. higher gut-fullness index) within the plume and front waters than in oceanic waters (Rissik & Suthers 1996). In addition, changes in freshwater input may alter the size of retention areas that may increase subsequent recruitment. Although changes in abundance of food and retention are positive effects, increases in numbers of predators such as jellyﬁshes are likely to be negative. Larval ﬁshes of some taxa are more abundant in the vicinity of river discharge plumes and associated fronts. Densities of larval ﬁshes were up to 18 times and ﬁsh eggs up to 33 times more concentrated in fronts than in plume and ocean water (Grimes & Kingsford 1996). The primary mechanism contributing to increased abundance in plumes was hydrodynamic convergence, although adults may also seek the most productive waters in which to spawn (Sabates & Maso 1990, Govoni & Grimes 1992). Assemblages of larval ﬁshes may vary between plume, front and open-ocean. For example, carangids were most abundant in plume waters, engraulids in frontal waters and exocoetids in ocean waters of the Mississippi River discharge plume (Grimes & Finucane 1991). Gobiids, pleuronectids, hemiramphids and blenniids were most abundant in the plume and front waters of Botany Bay (NSW, Australia), whereas exocoetids were most abundant in the front waters (Kingsford & Suthers 1996). The larvae of gulf menhaden (Brevoortia patronus) aggregate in the plume front (Govoni et al. 1989). The correlation between recruitment of gulf menhaden and Mississippi River discharge, however, showed an inverse association (Govoni 1997). Thus, when river discharge increased from year to year, recruitment decreased. Shoreward transport of larvae was probably prolonged by increased river discharge, making the period longer where larvae were vulnerable to predators (Govoni 1997). River discharge was also inversely associated with growth and survival of juvenile gulf menhaden near the Mississippi delta (Deegan 1990). Species in discharge plumes and fronts are likely to be more vulnerable to pollutants than those in the open-ocean (Kingsford & Gray 1996). There is mounting evidence that larvae are extremely vulnerable to pollutants and deleterious effects occur after exposure for <24 h 285

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(Kingsford & Gray 1996). Many studies involve laboratory tests of median lethal concentration and there is little information on how such data relate to the ﬁeld. Nevertheless, normal loads of freshwater in plumes and increased loads during storms may provide combinations of chemicals that can be toxic to larvae.

Birds Changes in freshwater ﬂow may also impact assemblages of waterbirds, including species that feed on plants, frogs and ﬁshes. The diversity of waterbirds is dependent on the diversity of habitats within and among systems of wetlands. Alterations to natural ﬂuctuations of water level may alter habitats and, in turn, assemblages of birds (Brock et al. 1999). Diversity of the avifauna is also dependent on the availability of food. For example, open water provides habitat for a range of diving birds and persistent open water may attract marine species such as pelicans and cormorants that feed on ﬁshes. Shallow meadows may attract wading species, although the main habitats in which waders are found are mudﬂats. Many biologically important wetlands support breeding of waterbirds that are listed as vulnerable, for example brolgas (Grus rubicundus), black-necked storks (Xenorhynchus asiaticus) and magpie geese (Anseranas semipalmata) (Kingsford 1999). Although the majority of research has focused on freshwater wetlands, many waterbird species using these habitats may also be found in estuarine and marine habitats (Gosper et al. 1983, Woodall 1985, Dorfman & Kingsford 2001). Unlike other taxa, birds are able to move as inland areas dry up. For example, increased numbers of Australian pelicans (Pelecanus conspicillatus) in coastal regions in one year were associated with a return to the coast as inland areas dried up (Woodall 1985). Many studies have found rainfall inﬂuences waterbird numbers and movement (see references in Woodall 1985). The turbidity of estuaries may be inﬂuenced by freshwater input, and this could alter the ability of diving and wading birds to detect ﬁshes. Thus, turbid waters may provide ﬁshes with protection from piscivorous birds (Bruton 1985). Tidal and wind mixing, sediment re-suspension, ﬂocculation of ﬁne particles due to mixing of fresh and salt water and areas that trap silt and sediments are likely to interact with river inﬂow to ensure that patterns of turbidity in estuaries are complex. Worldwide, brackish water wetlands are a vital resource for birds. Wetlands may be lost because water removal from rivers robs wetlands of their source of water and sediment affecting many habitats (see p. 254). Wetlands in arid parts of the world are particularly vulnerable due to the scarcity of water. Reducing freshwater inﬂow may reduce available foraging and nesting habitat for birds resulting in reduced numbers.

Other vertebrates Seagrass habitats in some areas of the world provide essential food for marine vertebrates. For example, in Queensland (Australia) dugong (Dugong dugon) and green sea turtles (Chelonia mydas) feed on seagrasses (Preen et al. 1995). Dugongs disappeared after the seagrasses died (Preen et al. 1995). Likewise, manatees may be found in coastal marine areas including salt marsh (Baugh et al. 1989). The distribution of manatees and crocodiles in Florida Bay and the southern Everglades has changed since the volume and timing of 286

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freshwater ﬂows to the region changed (McIvor et al. 1994). Therefore, effects from reduced ﬂows on habitat-forming species may be widespread and have signiﬁcant effects even on large organisms.

Implications for managers Alterations to ﬂow of freshwater to estuaries and coastal environments clearly have a number of effects on habitat-forming species and on organisms within these habitats (Table 8). The effects are largely due to alterations in river ﬂow per se (i.e. changes in volume and sediment loads, as well as hydrodynamics) but also due to changes to chemical attributes of the water (i.e. salinity, temperature, nutrients and contaminants). Changes to habitat-forming species include loss, alterations to the type and reduced cover of habitats from effects on recruitment, growth and survival. Organisms within these habitats are also affected by changes to recruitment, growth, movement, mortality and fecundity. Before water management decisions are made there is a need to understand what impacts may occur (Table 8). Individual countries, through their water agencies, are generally responsible for water management of a river that ﬂows through their own country. Approximately 260 rivers, however, ﬂow through multiple countries (Postel 2000). Nine countries share the river basin of the Nile (Kashef 1981) and four share that of the Ganges (Khan 1996). Many countries depend on rivers ﬂowing into their territory from another country for a substantial amount of their water, yet for many river basins there is no treaty among all countries setting out water allocations and no allocation given for natural systems (Postel 2000). The classic dispute has been between India and Bangladesh with regard to the Ganges (Khan 1996). Although Bangladesh wanted to involve Nepal in negotiations, India objected to the involvement of a third country. Instead, India entered into a joint agreement with Nepal leaving Bangladesh out (Khan 1996). In principle, water resources should be shared equitably among riparian countries. Within Australia, proposals for water diversion are submitted to water agencies of government for each state or territory. Currently, the process of application, assessment and approval of applications does little to recognise potential ecological impacts at the appropriate scale of management (Kingsford et al. 1998), and shows little recognition that estuarine and marine habitats may be affected. For example, non-government conservation groups, other state agencies and downstream governments were unable to object legally to an irrigation application from the Paroo River (Queensland, Australia: Kingsford et al. 1998). Political boundaries do not make sense when it comes to managing water. The decision-making process needs to include policies and management at the scale of the catchment so that all those affected can be involved (e.g. Murray–Darling Basin Commission, Australia). Changes in catchments may affect not only estuarine but also coastal waters and the associated organisms. Perceptions that “water going to sea is wasted” (Loneragan & Bunn 1999) are at the expense of coastal ecosystems and marine industries (e.g. ﬁsheries). To determine whether an impact does occur from a change in freshwater input to an estuary or coastal region a monitoring programme is necessary. Monitoring and managing all aspects of biodiversity that are of interest will, however, be difﬁcult (Simberloff 1998). Managers sometimes use the status of indicator, ﬂagship, umbrella or endangered species for management. Problems with single-species management have led to the idea of ecosystem 287

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Table 8 Summary of potential impacts of changes in ﬂow of freshwater on estuarine and coastal environments. The table has been divided into effects of reduced ﬂows and increased ﬂows. For further details on seagrasses, algae and invertebrates see Tables 4, 5 and 6. Factor Reduced ﬂows Decreased ﬂow

Decreased water levels Geomorphology Salinity Salinity more stabile Increased salinity of water column

Temperature Increased temperature Sediments

Reduced sediments Increased sediments

Decreased nutrients Contaminants

Potential impact Drain salt marsh leading to habitat loss Nutrient deﬁciencies and excessive soil oxidation in marsh habitats Reduced germination, biomass and density of some seagrasses Upstream migration of ﬁshes prevented No trigger for ﬁshes to spawn, recruit or move Reduced recruitment of ﬁshes to estuaries Reduced ﬁsheries production Reduced foraging and nesting habitat for birds Exposure of submerged vegetation and die-back Estuary mouths close more frequently and for longer times Potential for hypersaline waters to develop Seagrasses colonise upper estuarine areas Seagrasses displace brackish macrophytes and salt marsh Decline of some seagrasses Die-back of mangroves Limit growth rates of algae making them more vulnerable to grazers Decreased growth and mortality of corals Increased mortality of oysters due to disease Increased number of species, density and biomass of marine ﬁsh in estuaries Temperatures may increase or decrease depending on environment Possible coral bleaching Increased stability in estuaries Increased sediment volumes in estuaries due to less ﬂushing OR decreased sediment due to increased deposition in rivers Increase in biomass of seagrasses Decline in species richness and reduced cover of seagrasses Decrease in turf-forming algae Decrease in recruitment, growth and survival of macroalgae Reduced growth and recruitment of corals Smothering of corals leading to bleaching and death Effects on abundance and composition of meiofauna Interference with feeding by invertebrates Mortality of invertebrates from smothering Algae may use nutrient stores so difﬁcult to see effect Decreased production of oysters Contaminants possibly more concentrated

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Table 8 Factor Increased ﬂows Increased ﬂow

Major ﬂoods

Decreased salinity

Increased sediment Increased turbidity (and decreased light)

Increased nutrients

Reduced dissolved oxygen in bottom waters Increased contaminants

continued

Potential impact Low salinity waters; water column stratiﬁed Water logging of salt marsh and mangrove habitats; mortality possible Abrasive ﬂows Change to seagrass assemblage structure Increased biomass of phytoplankton including possible phytoplankton bloom Horizontal and vertical distribution of zooplankton affected Decreased species diversity of zooplankton Increased abundance of jellyﬁsh BUT excessive runoff led to a decline in abundance Decreased catches of oysters BUT links to nutrients Higher mortality and failure of recruitment of crabs near freshwater input Decreased recruitment of some species of ﬁshes Mortality and removal of macroalgae Mortality of benthic invertebrates Triggers spawning in some ﬁshes Death and diseases in invertebrates and ﬁshes, due to acidiﬁcation or hypoxia (see below) Death of macroalgae in shallow water Mortality of corals and change in structure of assemblages Decrease in coral cover Decrease in abundance and number of species of meiofauna Reduced growth rates of invertebrate larvae See under reduced ﬂows Increased epiphyte loads Decreased biomass and growth rates of seagrasses; decline in species richness Possible clogging of gill ﬁlaments and gill rakers of ﬁshes Decline in ability of piscivorous birds to catch ﬁshes Decline of seagrasses OR increased biomass and distribution of seagrasses No effect on epilithic algae Possible indirect effects on corals through increased algae Increased abundance of some invertebrates (e.g. prawns) Mortality of invertebrates and ﬁshes due to hypoxia Reduced numbers of organisms in sediment Larvae vulnerable to contaminants

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management, which would then focus on ecological processes rather than individual species (Simberloff 1998). An emphasis on processes also leads to a focus on broad spatial scales, a holistic view, consideration of humans as part of the ecosystem and adaptive management as the scientiﬁc basis (Simberloff 1998). Conservationists, in particular, are concerned with ecosystem management because many ecosystem processes can be maintained even as species disappear, the boundaries of an ecosystem are not always apparent and there is an absence of a consensus for a deﬁnition of ecosystem management (Simberloff 1998). Recently, Simberloff (1998) has suggested that a more effective approach to management may be the concept of keystone species, as this provides a slightly different orientation to single-species management. A keystone species approach focuses on understanding the mechanisms that underlie function and structure of an ecosystem and thus may combine attractive features of single-species management and ecosystem management. We are not, however, aware of any demonstrated outputs using this approach. We found strong evidence that physical and biological changes that were attributed to variation in water ﬂow invariably affected habitats within coastal ecosystems and, therefore, the ecosystems themselves. We endorse, therefore, monitoring approaches that focus on habitat-forming organisms and the biologically (e.g. keystone species) and socially relevant organisms (e.g. ﬁsheries species) that are associated with them. It is difﬁcult to predict with certainty the ecological effects of water management activities or determine the efﬁcacy of measures aimed at regulating them. However, every major change in management policy or development on a river is in fact a perturbation experiment (see p. 292). Most theoretical literature on resource management assumes a passive strategy is best, thereby focusing on one choice of policy as being best (Walters & Holling 1990). Thus, historical data are used to construct a best estimate for the response, and the choice of management decision assumes that this model is correct (Walters 1986). However, an active adaptive strategy can be used, in which data available at each time are used to suggest a range of alternative response models and a policy is selected that reﬂects a balance between short-term and long-term values of which model is correct (Walters 1986). Thus, the relative plausibility, or probability of a variety of alternative models is considered. Bayes’ theorem rather than standard frequentist statistical methods are frequently used. We suggest that some variation on the active adaptive strategy be considered for changes to water management, and that any strategy should include pulse and press ﬂows (see below). Another major issue in managing resources is uncertainty. There is often limited information on the effect of freshwater input to estuarine and marine systems. There must be, therefore, an element of uncertainty in predicting effects of changes in freshwater input. Lack of information and uncertainty is meant to lead to endorsement of the precautionary principle, so that until there is compelling evidence that there will not be a deleterious effect, a conservative approach to management should be used, that is, the long-term sustainability of the environment should be favoured. Despite this, many activities affecting freshwater input are proceeding without any understanding of the impacts to estuaries and marine systems, in large part due to economic and political pressures. Particular attention should be given to attempting to mimic the natural ﬂow regime so that there is some acceptable median ﬂow, as well as pulse events. Both types of ﬂow are important to sustaining the physical environment as well as habitats and the organisms associated with them. Many studies that examined factors affected by freshwater input have been performed in the laboratory (Table 9). Extrapolating from laboratory or microcosm experiments to natural systems is not easy. While a microcosm experiment may allow unequivocal demonstration 290

Sediment Temperature Salinity Light/water clarity Water motion/ﬂow Nutrients

291

Fishes

3 3 3 3

3

3 3 3 3

3 3

Seagrasses

3

Macroalgae

3

Phytoplankton

3

3 3 3

Invertebrates 3

3 3 3

Fishes 3 3

Seagrasses 3 3 3 3 3 3

Macroalgae 3

3 3 3

Phytoplankton 3

3 3 3

Invertebrates 3

3 3

Seagrasses 3

3

3

3

3

3

Macroalgae

Fishes

Phytoplankton 3

3

Invertebrates 3

Scale of ﬁeld experiment

N/A N/A N/A N/A N/A N/A

Fishes

Field experiment

m N/A N/A m N/A km

Seagrasses

Laboratory experiment

m N/A N/A m N/A m

Macroalgae

Correlative or descriptive

Phytoplankton N/A N/A N/A m N/A m

N/A N/A N/A N/A m N/A

Invertebrates

Table 9 Summary of research on factors affected by freshwater input and their effects on ﬂora and fauna. Ticks indicate that studies have been done; the spatial scale of ﬁeld studies is also indicated as m (metres), km (kilometres) or N/A (not applicable because ﬁeld studies have not been done).

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of the effects of a speciﬁc factor such as salinity, natural systems are larger in scale and more complex. This complexity is likely to mean that the ﬁndings of a microcosm experiment are not directly applicable to natural systems (see also Greenwald & Hurlbert 1993). For example, Greenwald & Hurlbert (1993) monitored microcosms and natural events in a lagoon, and found differences in physical-chemical characteristics as well as taxonomic composition of phytoplankton and zooplankton. We suggest that laboratory experiments are a ﬁrst step towards understanding natural systems but should, where possible, be followed up with manipulative ﬁeld experiments. We do, however, recognise the difﬁculty of manipulating factors such as salinity in the ﬁeld. Microcosms and/or mesocosms, for example, are likely to be the only experimental approach for determining complex interactions between such factors as salinity, temperature and nutrients. The inability of researchers to manipulate systems over large areas and to control changes in freshwater input impedes our understanding of the effect of freshwater input on estuarine and marine systems. Many of the manipulative ﬁeld experiments have been carried out at small scales (Table 9). Such experiments provide important insight into the processes operating at small spatial scales but experiments are needed to determine responses at large scales. Underwood (1996) suggested that when events create disturbances we should use this as an opportunity for an experiment. As an example, he suggested that pollution from urban runoff through storm water drains could be studied experimentally because the drains already exist and all that was needed was comparison with similar pieces of habitat where there are no drains. Dams are another obvious example for such an approach. The needs for such experiments should be explained to regulatory and managerial agencies so that opportunities do not go astray. Changes in water management are usually made at the level of individual rivers. Individual rivers (and their catchments) can therefore be used as experimental replicates and other rivers in which no changes in water management are made can be used as controls or replicates. We acknowledge that there are signiﬁcant problems in selecting rivers or estuaries as replicates so that biogeographic variation or other factors that differ between rivers and estuaries do not confound interpretations. In addition, it is known that changes to water management (e.g. removal of water for irrigation) will occur before they happen. Thus, monitoring of both impact and reference locations can occur before any change is made and continue after the change has occurred (e.g. BACI or MBACI designs, Underwood 1991, 1992, 1993, Keough & Mapstone 1995). The potential impact can then be assessed by determining whether the temporal difference (from before to after a disturbance) varies between the disturbed area and similar undisturbed areas (reference areas). These sampling designs are appropriate where univariate analyses are needed. Where multi-species assemblages are involved, comparable analyses are only just being developed (e.g. Anderson 2001). Past multivariate analyses (e.g. ANOSIM, Clarke 1993) could not test interactions, which is the focus for detecting potential impacts. Results from experiments will not happen quickly. The rate of progress in ecology is slower than in other ﬁelds because experiments take longer, replication, control and randomisation are harder to achieve, and ecological systems change over time (Hilborn & Ludwig 1993). Stochastic variation, especially in Australia, ensures that river ﬂows and climatic patterns are so variable that the time frame for experiments needs to be extremely long. Therefore, the timescale of monitoring will need to be over many years. There is a substantial body of literature that suggests that freshwater has a strong inﬂuence on the nature of estuaries, and pelagic and benthic habitats that extend over the continental shelf. Many important commercial species are supported by these environments and freshwater 292

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input may inﬂuence their abundance. Many of the problems regarding freshwater input to estuaries and marine systems relate to escalating conﬂicts among development, environmental protection and management of natural resources, growing numbers of freshwater users and conﬂicts among them, and multiple authorities managing the resource. Despite predictions that inﬂow to wetlands, estuaries and the sea will be reduced, some consider that a cubic metre of river water used for irrigation would bring far more value than the same cubic metre delivered to an estuary. Such comparisons are based on economic gains from irrigated land against estuaries. However, if we have a limited understanding of the impact of reduced ﬂows on estuaries such arguments are ﬂawed (see also Toman 1998). Catches of prawns (and income) from the Zambesi (southern Africa), for example, are positively correlated with freshwater input from the river. Despite this awareness, ecosystem services derived from estuaries are not adequately quantiﬁed compared with those derived from irrigation and, therefore, they are often given too little weight in policy decisions (Costanza et al. 1997). There is a clear need for change. Finally, freshwater is a common-property resource, which is owned by no single person or organisation. Thus, no exploiter has reason to conserve it. As eloquently put by Lee (1993) “that which belongs to all is cared for by none”. Rivers and the water that they bear, are resources held in common, yet when exploitation of common resources is uncontrolled, the beneﬁts are often unequally shared by all. It is clear, however, that concerns about freshwater resources should not be restricted to the land as fresh water has a major inﬂuence on the physical and biological nature of coastal ecosystems.

Acknowledgements We are extremely grateful to C. Grimes, J. Govoni, R. Kingsford, J. Kirkwood, K. Pitt, D. Rissik and J. Robbins for reviewing the manuscript and providing many constructive comments. We thank J. Hughes for assistance with ﬁgures and tables.

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Oceanography and Marine Biology: an Annual THE PARADOX OF THE SPECI ES- R I Review C H D E2002, E P - S40, E A311–342 FLOOR © R. N. Gibson, Margaret Barnes and R. J. A. Atkinson, Editors Taylor & Francis

A RIOT OF SPECIES IN AN ENVIRONMENTAL CALM: THE PARADOX OF THE SPECIES-RICH DEEP-SEA FLOOR PAUL V. R. SNELGROVE 1 & CRAIG R. SMITH 2 Biology Department and Fisheries Conservation Chair, Memorial University of Newfoundland, St. John’s, Newfoundland, Canada A1C 5R3. e-mail: [email protected] 2 Department of Oceanography, University of Hawaii, Honolulu, Hawaii 96822, USA. e-mail: [email protected] 1

Abstract Deep-sea ecosystems are the most extensive and remote ecosystems on Earth. Perception of the deep-sea benthic environment has changed dramatically in the last century from one of an azoic, or at least species-poor habitat to one that is rich in species. The early misconception was created, in part, by evidence of vast, monotonous expanses of cold, dark sediment plains with little obvious spatial or temporal heterogeneity. Given that many species-rich ecosystems on Earth are obviously heterogeneous, it is surprising that some estimates of species numbers in the deep sea (e.g. ∼107 macrofaunal species) rival those for tropical rainforests. Although other estimates are more conservative (e.g. 5 × 105 macrofaunal species), it is clear that deep-sea benthic habitats contain many species. The paradox of high deep-sea diversity has generated a number of explanatory hypotheses, including some that are currently difﬁcult to test and others that are the focus of ongoing study. Approaches include analyses of local, regional, and global patterns, and experimental manipulations within habitats. Mechanistic generalisations are difﬁcult to make because experimentation and sampling coverage are spatially and temporally limited, but evidence to date suggests that small-scale habitat variability and patchy disturbance, as well as global and regional variability, may play roles in maintaining deep-sea diversity. The importance of small-scale habitat variability and patchy disturbance has been demonstrated for only a small subset of species, many of which are opportunists. Broad inferences from global and regional patterns of species diversity are debatable because many areas remain poorly sampled and causes of patterns are ambiguous. Nonetheless, our understanding of diversity patterns in the deep-sea benthos has increased dramatically in the last three decades. If the approaching decades hold even a portion of the surprises seen in the recent past, then science can expect very exciting discoveries from the deep ocean in the near future.

The deep-sea benthos The deep-sea bottom is remarkable among Earth’s habitats. It is vast, accounting for more than half of the Earth’s solid surface (Gage & Tyler 1991). Sunlight is completely absent, temperatures are low and ambient pressure is extreme. Unlike terrestrial ecosystems and, to a lesser extent, shallow-water ecosystems, the deep-sea benthos is almost entirely reliant 311

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on production that occurs thousands of metres above (vent communities being notable exceptions). Because the majority of organisms are strongly linked to the sediment substratum in which they reside, they are relatively ﬁxed in space, at least as adults, and dependent on phytoplankton and other food “raining” at low rates from remote surface waters. Much of this food has been recycled during its descent, arriving as faecal pellets or partially degraded detritus. Pelagic communities, by contrast, are ﬂuid in nature and function independently of a substratum other than the seawater itself. In addition, pelagic communities are fuelled by fresher and more “local” primary production. The contrast between terrestrial communities and deep-sea benthos is even more extreme. On land, the air medium is much less important as a habitat or biological transport medium, primary production is relatively local, and primary producers are often large and important contributors to habitat structure. Compared with most habitats on Earth, the deep-sea ﬂoor seems an unlikely place to ﬁnd large numbers of species, yet high biodiversity is becoming increasingly apparent.

Changing perceptions of deep-sea habitats and communities Historical perspective Perception of deep-sea communities has changed markedly since the exploration of the deep oceans began early in the nineteenth century (see Mills (1983) for a thorough review). In 1818, Sir John Ross recovered a basket star from a sounding line reportedly dropped to 1000 fathoms depth (although Rice (1975) noted that the actual depth at this site is 500–600 fathoms). Around 1840, J. C. Ross and J. Hooker found sounding line mud from the Antarctic continental slope at 1800 m to be “teeming with life”. These collections were not adequately described, and the general perception of the deep ocean as a dark, lifeless plain persisted. Early debate on life in the deep-sea was fuelled by Edward Forbes of Edinburgh University in the mid-nineteenth century, who suggested that an “azoic zone” existed beyond 600 m depth. Forbes based this conclusion on samples collected from the Aegean Sea, where life is indeed sparse, but others would soon provide evidence that there is ample life in the deep sea. In the late 1860s, Charles Wyville Thomson dredged life from 4300 m from waters around Britain, and Sars (1872) reported high species diversity between 400 m and 600 m from the Lofoten Islands, Norway. Sars’ material included a stalked crinoid belonging to a group previously thought to be nearly extinct. Upon seeing the material, Thomson suggested that the deep sea might be a refuge for ancient forms of life, a misconception that is only now disappearing from textbooks. But it was the HMS challenger expedition (1872–76) that established the generality that life occurred throughout the deep oceans, and the galathea expedition of 1950–52 that established that this was also true in the deep ocean trenches of deeper than 10 000 m. Sir John Murray of the challenger expedition also observed that deep-sea samples tended to contain more species and were characterised by less dominance than shallow-water samples (Murray 1895). Thus, early evidence that the deep sea is diverse was published and publicly discussed in the nineteenth century (see Carney 1997), but somehow the idea soon disappeared into scientiﬁc obscurity. 312

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Modern perspective Although the presence of life throughout the deep oceans was established before the turn of the twentieth century, the rich diversity of species in the deep sea was ﬁrst documented over broad areas by R. R. Hessler and H. L. Sanders (Hessler & Sanders 1967, Sanders 1968, 1969, Sanders & Hessler 1969). By sampling with a newly designed epibenthic sled, they demonstrated that the species richness of the macrofauna (organisms retained on a 300 µm or 500 µm sieve but too small to be identiﬁed in photographs) within a site in the deep sea was comparable to or exceeded available estimates for most other marine habitats. Much of this diversity was found in the small polychaete annelids, crustaceans, molluscs, and other invertebrate taxa that live within the sediments and are easily missed with coarse sampling equipment. Subsequent quantitative samples collected with box corers (Hessler & Jumars 1974) indicated that the epibenthic sled missed many taxa, and that the diversity of macrofauna in the deep sea is even higher than Hessler and Sanders had suspected (Grassle & Maciolek 1992). In addition, recent abundance estimates for the tiny nematodes, crustaceans, and foraminiferans making up the meiofauna (organisms >44 µm but <∼300 µm) suggest that they may even exceed macrofauna in total species number (Lambshead 1993). In summary, perception of the deep sea has evolved from one of a nearly azoic environment to one that is remarkably species-rich. A second changing perception of the deep sea concerns its degree of spatial and temporal homogeneity. Early oceanographic records indicated quite correctly that there is little ﬂuctuation in temperature and salinity in the deep sea, and the remoteness from light and surface productivity led to an obvious conclusion of aseasonality. Early bottom photographs documented vast rolling plains of sediment with little metre-scale structure, and combined with the lack of variability in environmental variables, low biomass, and a perceived lack of species and life, led to the analogy that the deep sea is “desert-like” – an analogy that persists in some textbooks today. Sanders (1969) hypothesised that this homogeneity might be a critical factor in promoting high deep-sea diversity. His stability-time hypothesis stated that in very stable environments, species become highly specialised within narrow niches, and therefore many species are able to co-exist at competitive equilibrium. Compared with most habitats on Earth, the deep-sea ﬂoor indeed appears to be more temporally and spatially homogeneous. This contrast is most striking in comparison with other species-rich habitats, such as rain forests and coral reefs, which display dramatic 3dimensional biogenic structure. The deep-sea lacks obvious spatial complexity. Nonetheless, there is increasing evidence that the deep sea is more variable in space and time than has previously been thought. Evidence for seasonality in many deep-sea systems became available during the early 1980s (Deuser & Ross 1980, Honjo 1982, Billett et al. 1983). Spatial variability over large scales, such as that created by intense deep-sea currents was documented by Hollister & McCave (1984). Wolff (1979) summarised some of the earlier records of ﬁner-scale (cm-m) variability in the occurrence of various types of terrestrially-derived materials (e.g., wood and coconut husks) which provide substrata for deep-sea invertebrates. Several more recent studies have suggested that species ranging in size from small benthic copepods (Thistle & Eckman 1990) to macrofauna (Levin et al. 1986, Bennett et al. 1994), are able to utilise a variety of biological structures ranging from sponge spicules (Bett & Rice 1992), to xenophyophore tests (Levin et al. 1986), to whale skeletons (Bennett et al. 1994). In summary, the deep sea is generally homogeneous in terms of temperature, physical disturbance and broad topography, but there is increasing evidence of temporal and spatial 313

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variability in particle ﬂux, erosive currents, biogenic disturbance and biogenic microhabitats. The importance of this variability is the focus of ongoing research. As a ﬁnal point, recent evidence also indicates that the deep sea is less stable over geological timescales than previously thought. Cronin & Raymo (1997) showed variation in deep-sea diversity on times scales of 103–104 yr that are consistent with glaciation cycles, suggesting that the perception of long-term stability in the deep sea may, in fact, be incorrect.

How diverse are deep-sea systems? What we mean by diversity Diversity may be viewed or measured in a variety of ways, depending on the speciﬁc question posed and the scale considered. Many use diversity and species richness interchangeably. However, for the purposes of this discussion species richness will be treated as a total count of species per habitat or unit area and diversity will refer to measures that somehow incorporate the apportioning of individuals among species as well. A commonly used diversity measure, and one that will be used for this discussion, is Sanders’ rarefaction index as modiﬁed by Hurlbert (1971), which is sensitive to rare species (Smith & Grassle 1977). Because many of the species collected in deep-sea samples are rare (e.g. Grassle & Maciolek 1992), Hurlbert rarefaction is commonly used to measure diversity in deep-sea studies. Hurlbert rarefaction can be used to generate a curve showing how species accumulate as additional individuals are collected from a given area; it also provides a useful means of comparing areas at a similar number of individuals. In deﬁning diversity, it is also important to differentiate between local and regional scales. Many estimates of biodiversity in deep-sea habitats are based on alpha, or withinpatch, diversity. Alpha diversity is operationally deﬁned as within-sample diversity (e.g. the diversity with a 100 cm2 core). At the other extreme is regional, or gamma diversity, where diversity estimates are based on pooling individual samples (and diversity) across different patch types within a region. Comparisons of diversity at regional scales are more difﬁcult than at local scales because the degree of pooling across patches may differ among the habitats being compared and rarefaction becomes more of a measure of evenness than richness as sample size becomes large (May 1993). A potential pitfall of rarefaction curves is that species numbers at the lower end of the curve may be overestimates because dominance may decrease with increasing sample size (Gray 1997). Nonetheless, if the entire curve is considered and the increasing impact of evenness acknowledged, rarefaction is the most suitable means of comparing diversity. Other diversity measures such as species per sample or total species collected per unit area are confounded by differences in numbers of individuals; collection of additional individuals invariably adds additional species regardless of the underlying species structure. Despite the difﬁculties in estimating regional diversity, there is tremendous interest in making comparisons between ecosystems. As such, rarefaction comparisons are used that, though not free of bias, are readily available from many deep-sea studies and thus amenable to direct comparison (see also the argument presented by Wilson (1998) ). The discussion is therefore divided into a review of studies comparing ecosystems, and therefore over broad scales, and a separate review of studies on diversity 314

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regulation in deep-sea systems. The latter includes some regional comparisons but focuses on comparisons of alpha diversity. This review will focus on ecological factors that allow maintenance of high diversity rather than the evolutionary mechanisms that have created modern patterns. The former factors may be difﬁcult to test but the latter are even more elusive.

Estimates of deep-sea species richness A number of recent studies have generated diversity estimates for different areas of the deep sea (Table 1). Considerable variability exists in local (or alpha) diversity, expressed as species 100 ind.−1, although for macrofaunal species in the deep sea the number averages somewhere around 50. Considerably lower numbers are seen in some areas, and in many instances there are overriding environmental variables that are likely to contribute to these low values. For example, Levin et al. (1991) noted reduced diversity in low oxygen areas of the seamount, Volcano 7, in the eastern tropical Paciﬁc, and Grassle (1986) noted reduced diversities in sediments in hydrothermal vent ﬁelds. High levels of organic input and associated reduction in sediment oxygen concentrations may also depress deep-sea diversity as noted in an upwelling area off southwest Africa (Sanders 1969) and off Cape Hatteras, USA (Schaff et al. 1992). Several different meiofaunal groups (e.g. nematodes) exhibit a similar number of ∼50 species 100 ind.−1, suggesting that when all meiofaunal groups are included, total meiofaunal diversity may be considerably higher than that observed for macrofauna. In addition, because meiofaunal abundance per unit area is much higher than for macrofauna, total meiofaunal richness will be higher within a particular patch. Reduced macrofaunal diversity was observed in several samples from the HEBBLE area south of Nova Scotia, which is subject to intense currents, but meiofaunal harpacticoid diversity was high (Thistle et al. 1985). The macrofauna (Gage et al. 1995) and meiofauna (Lambshead et al. 2001a) from a hydrodynamically active deep-sea site off Portugal also had reduced diversity relative to a low-energy site nearby. Thus, diversity of some faunal groups may be more sensitive to environmental perturbations that others. Diversity estimates have also been compared across depth and latitude. A parabolic relationship of regional diversity with depth has been noted in several studies in the Atlantic, with peak macrofaunal diversity at intermediate depths (e.g. Rex 1981, Etter & Grassle 1992). In examining meiofaunal nematodes, Boucher & Lambshead (1995) also found peak diversity at intermediate depths. Data from the Paciﬁc indicate that the parabolic depth-diversity generality may not be global because isopod diversity in the Central Paciﬁc (abyssal plain) may exceed that on continental slopes (Poore & Wilson 1993), and because reduced diversity occurs at slope depths within the oxygen minimum zone in the eastern Paciﬁc (Levin & Gage 1998). Within the Atlantic (North and South pooled), Wilson (1998) observed an increase in diversity for asellotan isopods with depth and a corresponding decrease in ﬂabelliferan isopods. A proposed negative relationship between diversity and latitude for some macrofaunal groups (Rex et al. 1993) was not observed in isopod data (Poore & Wilson 1993), particularly in the southern hemisphere. The relationship between diversity and latitude is stronger in the North Atlantic (Rex et al. 1993) but the relationship is signiﬁcantly weakened by removing data from the Norwegian Sea (a basin disturbed during the Pleistocene). A negative relationship with latitude has also been found for species richness in the North Atlantic 315

316

San Diego Trough (NE Paciﬁc)

Santa Catalina Basin (NE Paciﬁc)

North Carolina Slope NW Atlantic Slope

Rockall Trough (NE Atlantic) NW Atlantic Slope Central North Paciﬁc

Volcano 7, Tropical Paciﬁc Fieberling Guyot, Eastern Paciﬁc Argentine Basin Brazil Basin Sierra Leone Basin Angola Basin Guyana Basin Weddell Sea Norwegian and Greenland Seas Southeastern Australia NE Atlantic Slope NE Atlantic Slope

San Diego Trough (NE Paciﬁc) HEBBLE Area (NW Atlantic) San Diego, Rockall Troughs Porcupine, Madeira, Hebble etc. Puerto Rico NE Atlantic Slope

Macrofauna Total

Total

Total Total

Total Total Total

Total Total Isopods Isopods Isopods Isopods Isopods Isopods Isopods Isopods Tanaids Bivalves

Meiofauna† Harpacticoids Harpacticoids Nematodes Nematodes Nematodes Foraminifera# 1220 4626 200–2000 2000–6000 >6000 1350

770–1000 580–635 ∼5000+ ∼5000+ ∼5000+ ∼5500 ∼4000+ ∼4000+ 800–3617 200–3150 3400 3400

2875 3600 5500–5800

583–3015 1800–2100

1240–1300

1230

Depth (m)

45–48 43–51 37– 43‡ 35–39‡ 26–34‡ 28–50

10 35– 40 10–38 37–39 23–32 8–26 26–34 22–23 3–15 17–35 19 12

56 38

15–63 52

24–34

42

Species 100 ind.−1

– – – –

*

– – – – – – – – – – – –

110 148 21

∼50–250 127

>70

144

Species 0.25 m−2

Thistle 1983 Thistle 1983 Boucher & Lambshead 1995 Boucher & Lambshead 1995 Boucher & Lambshead 1995 Gooday et al. 1998

Jumars 1976, Jumars & Hessler 1976 Smith 1986, Kukert & Smith 1992 Blake & Grassle 1994 Grassle & Morse-Porteous 1987, Grassle & Maciolek 1992 Gage 1979 Grassle & Morse-Porteous 1987 Hessler & Jumars 1974, Jumars & Hessler 1976 Levin et al. 1994 Levin et al. 1994 Poore & Wilson 1993 Poore & Wilson 1993 Poore & Wilson 1993 Poore & Wilson 1993 Poore & Wilson 1993 Poore & Wilson 1993 Svavarsson et al. 1990 Poore & Wilson 1993 Gage et al. 1995 Gage et al. 1995

Reference

* Thistle (1978) collected 140 harpacticoid species in a total sample area of 0.14 m2. † Tietjen (1992) collected an average of 70.1 species of meiofaunal nematodes per 0.0060 m2 at three abyssal stations (3520–5050 m) in the Venezuela Basin. Only one of the 136 total species was known to science! ‡ Based on only Expected Species for 91 individuals and may therefore underestimate by ∼5%. # Includes macrofaunal and meiofaunal sized individuals.

Site

Expected species of macrofauna and meiofauna per 100 individuals, and species per 0.25 m2 in soft-sediment, deep-sea sites.

Taxon

Table 1

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(Rex et al. 2000), and for species richness of Foraminifera in the North and South Atlantic (Culver & Buzas 2000). Lambshead et al. (2000) argue that North Atlantic nematodes show a positive latitudinal gradient, and although Rex et al. (2001) express concern that sampling depth may have confounded the interpretation of nematode latitudinal trends, Lambshead et al. (2001b) present a convincing rebuttal that latitude was the more important variable and that the pattern is consistent with the known ecology of nematodes. Clearly, as more and more areas of the deep sea are sampled, some seemingly simple patterns become more complex. The depth and latitudinal patterns in diversity that have been observed for different taxa (and sometimes from different areas) are therefore not always consistent, and whether this variation is a result of geographic variability or differences in the responses of taxonomic groups is difﬁcult to assess. Differences in evolutionary history among groups (e.g. Wilson 1998) further complicate the comparison. The reality of the situation is that funding has rarely permitted examination of more than one group (e.g. isopods, nematodes) in a given study and the result is a checkerboard of data from around the world, often with mismatched pieces. Recently, attempts have been made to estimate the total number of species in the deep sea. Grassle & Maciolek (1992) collected samples off the coasts of New Jersey and Delaware, USA totalling 21 m2, which represents the most intensive deep-sea sampling effort in a single region to date. Using a rarefaction approach, Grassle & Maciolek (1992) looked at rate of macrofaunal species accumulation as additional samples were collected along a 2100 m depth contour. They estimated that after an initial rapid rise, species would be added at a rate of ∼1 species km−2 and assuming, conservatively, that species would accumulate at a similar rate across depth contours as along depth contours, they estimated that ∼108 macrofaunal species live within the 3 × 108 km2 of ocean ﬂoor deeper than 1000 m! Acknowledging that the deepest areas of the oceans probably accumulate species at a lower rate, because densities are an order of magnitude lower, they reduced the estimate to 107. May (1992) questioned the linear extrapolation of the rarefaction curve, and suggested looking instead at the fact that about 50% of the species in their study were new to science. Using this approach, he concluded that about half of the deep-sea fauna remains to be described and that total species number is unlikely to exceed 5 × 105 (double the number of described marine species). Poore & Wilson (1993) used isopod data from the Australian slope and elsewhere to suggest that Grassle & Maciolek’s (1992) study area may not be as species-rich as areas such as the South Paciﬁc. Indeed, if data from isopods can be extrapolated to all taxa, the northwest Atlantic area where Grassle & Maciolek sampled may be among the less species-rich deep-sea habitats! Using a similar approach to that of May (1992), Poore & Wilson (1993) suggested that because 5%, rather than 50%, of deep-sea macrofaunal species were known to science, an extrapolation to a global estimate of 5 × 106 macrofaunal species in the deep sea was appropriate. For meiofaunal nematodes, Lambshead (1993) estimated 108 species globally by scaling up Grassle & Maciolek’s (1992) calculation and factoring in the considerably higher densities of nematodes relative to macrofauna. Clearly, the accuracy of these estimates of total species in the deep sea is limited by the underlying assumptions, and by the relatively small proportion of the habitat that has been sampled. Paterson (1993) estimated that a total of ∼2 km2 of ocean ﬂoor has been sampled globally for macrofauna and Lambshead (1993) estimated that ∼5 m2 has been sampled for meiofauna. Huge areas of the oceans, particularly those in the South Paciﬁc and Indian Oceans, present vast unknowns. Given that there are ∼105 species described for marine sediments (Snelgrove et al. 1997), something between 95% and 99.9% of the fauna appear 317

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to remain undescribed. Even among those species that have been described, there is increasing evidence that “cosmopolitan” species in the oceans may, in fact, be sibling species (reviewed by Knowlton 1993) and we may therefore be underestimating species richness, even in the best known deep-sea habitats. Molecular biological approaches are revealing complex and previously unseen patterns in species and genetic diversity in deep-sea environments (France & Kocher 1996, Chase et al. 1998), adding further challenges to evaluation of diversity. Clearly, there is much work remaining to characterise the patterns and scales of species diversity on the deep-sea ﬂoor.

How does deep-sea species diversity and species richness compare with other systems? At the phylum or class level, there is little doubt that marine systems, and the deep sea in particular, have substantially higher richness than their terrestrial or freshwater counterparts. Marine ecosystems contain 90% of all animal families and 28 of 29 non-symbiont animal phyla occur in marine environments; of these 29, 13 are exclusively marine (Ray & Grassle 1991). Only one animal phylum, the Onychophora, has no living representative in marine habitats. At the species level, the question is more complex. Acknowledging the large numbers of assumptions in the various estimates of species numbers within different habitats, several conclusions can be drawn from various comparisons (Fig. 1). The data in the left panel were assembled largely by asking taxonomists specialising on different groups to estimate what portion of their taxon remained undescribed. The estimate in the central panel (Erwin 1982) was produced by looking at the numbers of beetle species typically associated with a given species of rainforest tree, estimating the proportion of the insects made up of beetles, and then multiplying by the estimated numbers of species of tree in the tropics. The criteria for the estimates in the right panel were described earlier. So what do these numbers tell us? First, estimates of total richness within terrestrial and freshwater systems generally fall below those for marine sediments. Marine scientists may be either wildly optimistic concerning the number of new species to be found, or there may be real differences in actual numbers of undescribed species. It should also be noted that the estimates for marine sediments are driven largely by species numbers in deep-sea habitats. The comparison of deep sea and shallow systems has recently been a subject of lively debate, and will be discussed separately. Another attribute of these estimates is that terrestrial and freshwater scientists believe that they have described a greater portion of their taxa than marine scientists. Given the difﬁculty in accessing many marine habitats, this may well be true, although in the nonmarine realm some groups such as nematodes and some habitats such as rainforests are also poorly described (Brussaard et al. 1997). This shortcoming is conﬁrmed by the large proportion of undescribed species in Erwin’s (1982) estimate for species richness of tropical insects. May (1992) suggested that about 15% of the species on Earth are found in the deep oceans, whereas the proportion moves closer to 75% or more if the marine estimates by Grassle & Maciolek (1992) or Lambshead (1993) are used with estimates other than Erwin’s. Regardless of which number is correct it is clear that the deep sea contains a non-trivial portion of the global species pool and most of the phyletic diversity (Ray & Grassle 1991). How do deep- and shallow-water marine systems compare? Over the last two decades, a number of studies and reviews have suggested that deep-sea systems are more diverse than their shallow-water counterparts, at least in terms of macrofauna. Indeed, an entire literature 318

THE PARADOX OF THE SPECI ES- R I C H D E E P - S E A F L O O R

0

Soils & Sediments (all taxa)

Tropical Insects

Poore & Wilson (1993)

May (1992)

Grassle & Maciolek (1992)

Thorson (1971)

Erwin (1982)

108 nematodes?

109 bacteria? Marine3

3

Marine (Described)

Terrestrial2

Terrestrial (Described)

10

Freshwater1

20

2

1

30

Freshwater (Described)

Number of Species (x 106)

Global Biodiversity Estimates

Deep-Sea Sediments (macrofauna)

Figure 1 Bar chart of various estimates of global species numbers for different environments. The criteria for the estimates are given in the text. Numbered sources in the left panel are 1 Palmer et al. (1997), 2Brussaard et al. (1997), 3Snelgrove et al. (1997). For each of these sources, the numbers of described and projected species are given. The arrow in Snelgrove et al. indicates estimates if bacteria or nematodes are included; data on which estimates for these groups are based are very limited. Values in other panels are projected numbers of species. Numbers for marine systems are indicated by solid bars. Freshwater and Terrestrial refer to species number for all global components of those environments pooled.

on “why deep-sea systems are so diverse” has been generated, based on relatively few comparisons between deep- and shallow-water systems. More recently, this generalisation has been questioned with data from several areas of the world (Gray 1994, Gray et al. 1997). One of the critical issues in comparing diversity in different environments is choice of sieve size. In deep-sea macrofaunal studies, a standard of 300 µm or 500 µm has been used in most recent studies, although 1-mm sieves are often used in shallow-water studies. Several studies have documented that 1-mm sieves can miss as much as 70–90% of macrofaunal individuals (Bachelet 1990, Schlacher & Wooldridge 1996) in shallow-water environments. This loss includes individuals of small species, but also includes juveniles of larger species. Thus, interpretation of how individuals are distributed among species is very difﬁcult. For example, samples processed over a 1-mm sieve will overestimate the number of species per 100 individuals because of the loss of juveniles (Warwick & Clarke 1996). Samples processed over coarse sieves also tend to have lower species richness but higher diversity and evenness (Bachelet 1990). It could be argued that the smaller size of deep-sea organisms may offset this bias, but the 300 µm sieve used in many deep-sea studies is considered a fairly comprehensive sieve choice by deep-sea ecologists. The critical issue is not whether shallow or deep-sea environments contain more total species; deep-sea areas cover a far larger area of the Earth and, therefore, very likely possess 319

PAUL V. R. SNELGROVE & C R A I G R . S MI T H

a higher total species count than shallow water. Species per unit area is complex; densities of organisms in many deep-sea environments are usually substantially lower than in many shelf environments. Shallow-water environments may therefore sometimes contain more species per unit area, even if species accumulate at a much slower rate per number of individuals. The more compelling question is whether there are any fundamental differences in diversity patterns between shallow- and deep-sea habitats. In general, there appear to be differences; examination of individual deep-sea samples seems to suggest that they generally have a more even distribution of individuals, faster accumulation of species with individuals, and often a higher proportion of singletons in a given sample than their shallow water counterparts. Indeed, in deep-sea samples, typically no macrobenthic species makes up >10% of the total abundance (Hessler & Jumars 1974, Grassle & Maciolek 1992) and each of the vast majority (>90%) of species accounts for <2% of total individuals (Grassle & Morse-Porteous 1987, Grassle & Maciolek 1992, Kukert & Smith 1992, Snelgrove et al. 1994). Deep-sea macrofaunal samples typically yield 24 –56 species per hundred individuals collected, and over 100 species per 0.25-m2 quadrat of sea ﬂoor (Table 1). By comparison, a typical intertidal or shallow marine assemblage of soft-sediment macrobenthos contains 8–24 species 100 ind.−1 (Sanders 1968, Oliver et al. 1979, Smith & Brumsickle 1989, Smith & Kukert 1996), a dominant species representing ∼10–25% of total individuals (e.g. Gray 1994) and fewer than 50 species per 0.25-m2 quadrat (Whitlatch 1977, Smith & Kukert 1996). Present data suggest that, in general, deep-sea communities are more diverse than most shallow-water communities (Sanders 1969, Gage 1996) in terms of alpha or within-sample diversity, but comparative data are not extensive and additional quantitative information is needed. On regional scales, conﬂicting arguments exist, likely in part because of the differences in scales and methods of sampling in deep- and shallow-water environments. For example, Gray (1994) argues that the continental shelf of Norway may contain as many species as Grassle & Maciolek’s (1992) New England Slope region. However, Gray’s broad geographical dataset (spanning 9° of latitude and >1000 km) cuts across a wide array of sediment types, depth zones, and latitudes, resulting in addition of new regional faunas as samples are added (Gage 1996). The breaks in Gray’s (1994) species-individual curve (see his Fig. 1) attest to this fact. In contrast, Grassle & Maciolek (1992) worked in a much smaller (176-km long), relatively homogeneous habitat (largely along a single depth contour as well) and their data do not indicate sharp faunal discontinuities (as conﬁrmed through clustering analysis). Thus, while total species counts may be comparable between the studies, one deep-sea region is compared with multiple shelf environments. Another point made by Gray (1994) is that dominance, as expressed by the proportion of the most abundant species, when averaged over his entire study area is comparable with dominance in the deep sea. He also agrees that this is a result of averaging, which masks the considerably higher dominance usually seen in shallow-water samples when compared with that seen in samples from the deep sea. The 1-mm sieve size used in Gray’s study is relevant for reasons described above, but it has particular relevance to dominance because smaller individuals of a taxon are underestimated by coarse sieves (e.g. Bachelet 1990, Warwick & Clarke 1996, Schlacher & Wooldridge 1996). In short, although total species richness may be comparable between the two studies, alpha diversity is substantially higher on the North Atlantic slope than on the Norwegian shelf, as is species richness within a single habitat. A more compelling case for comparable shallow-water and deep-sea diversity is presented by Gray et al. (1997). The data from Gray et al. (1997, Fig. 1), are reproduced here, along 320

Cumulative number of species

THE PARADOX OF THE SPECI ES- R I C H D E E P - S E A F L O O R

800

600

Bass Strait, Australia

New England Slope

Port Phillip Bay, Australia

Jøssingfjord, Norway

Frigg, Norway

400 Snorre, Norway

200

20

40

60

2

Total area sampled (m ) Figure 2 Curves of species number versus total area sampled as reproduced from ﬁgures in Gray et al. (1997) and Grassle & Maciolek (1992). All sites are shallow except the New England slope data from Grassle & Maciolek (1992) shown as a dotted line.

with data from Grassle & Maciolek (1992), to compare the shallow areas of Gray et al. with the New England deep-sea site (Fig. 2). With the exception of the Bass Strait data, all of the shallow-water curves appear to be levelling off well below the New England 2100 m slope, suggesting higher regional diversity in the deep sea. Nonetheless, the species diversity of Bass Strait (an area of coarse sand in 11–51 m of water in temperate southern Australia) exceeds that of the New England slope (noting Poore & Wilson’s (1993) data suggesting that the New England slope may be relatively low in diversity compared with many other deep-sea habitats). Given that most shallow-water sampling has been concentrated in the North Atlantic, we may indeed have a biased perception of shallow-water sediments in general. Gray’s review accentuates the fact that species richness of shelf communities may be underestimated. The data reviewed by Gray et al. (1997) also suggest that shallow areas with strong seasonality have somewhat reduced diversity, whereas those with less seasonality may indeed be species-rich. Sanders’ (1969) arrived at similar conclusions, ﬁnding that all but the shallowest communities from the Friday Harbor, Washington, USA region rivalled his deep-sea assemblages in species richness. For shallow-water sediments, we have reasonable explanations, based on habitat heterogeneity, for those areas that support many species (Bass Strait is a notable exception). Rapid spatial and temporal shifts in grain size, temperature, salinity, and organic ﬂux are all well documented in shallow water, providing alternate niches for a variety of species. In the deep sea, however, habitat heterogeneity is not nearly as obvious. Thus, we must explain extraordinary species richness in the face of limited habitat variability (at least on local scales) and very low population densities. As a consequence, mechanisms of diversity maintenance in the deep-sea have been the subject of speculation for decades. 321

PAUL V. R. SNELGROVE & C R A I G R . S MI T H

What data do we need to estimate diversity and pattern? The lively debates sparked largely by Grassle & Maciolek’s (1992) study point to several obvious shortcomings in understanding deep-sea diversity. First and foremost, sampling efforts of the intensity and quality of their study are needed elsewhere in the deep sea in order to explore the generality of high deep-sea diversity. Some of these sampling efforts must be directed towards the vast areas of the South and equatorial Paciﬁc where limited, but very diverse, samples suggest that the North Atlantic may actually be relatively speciespoor by deep-sea standards (Poore & Wilson 1993). As coverage of previously unsampled areas is achieved, we should be able to determine whether the proportion of undescribed species is closer to 50% or 0.5% on a global basis. Such sampling would considerably improve on one approach to estimating total species number. One other ideal, though perhaps unattainable goal, is to sample an area of the deep sea well enough for species versus abundance curves to approach asymptotes at multiple scales (e.g. km scale to 100s of kms). This would provide a second means to improve estimates of total deep-sea species richness. In terms of taxa, there is a pressing need to improve estimates for meiofaunal species number; Lambshead’s (1993) estimate of 108 species of nematodes swamps macrofaunal estimates and its validity and generality must therefore be tested. Bacteria and Archaea also represent an unknown, but potentially enormous, species pool as indicated by the planktonic species data of Giovannoni et al. (1990). Because essentially all marine taxa are more species-rich in the benthos than in the water column, because microbial habitat complexity is much greater in sediments than in the water column, and because the bulk of marine microbes actually reside in the sediments, a vast diversity of microbial species is likely to be discovered in deep-sea (and shallow-water) sediments (see also Fuhrman & Davis 1997). For all marine habitats, including shallow-water systems, it is necessary to move beyond the previous reliance on samples from the North Atlantic if better estimates of global species numbers and pattern are to be achieved. Better evaluation of diversity patterns will also lead to greater understanding of diversity maintenance because current broad-scale comparisons are based on a very limited number of data points (e.g. Rex et al. 1993, Poore & Wilson 1993).

Why are deep-sea communities diverse? Overview on theories and testability A number of theories have been proposed to explain the high diversity of deep-sea environments; these theories will be brieﬂy reviewed before considering the available data. Many of these hypotheses are not mutually exclusive and existing data are often consistent with more than one hypothesis. To a large extent this is also a result of the fact that it is likely that no single process or attribute of the deep-sea environment is entirely responsible for maintaining high deep-sea diversity. Thus, in presenting the available data a case will be made for multiple contributing factors, and experimental and sampling approaches will be suggested that may help to elucidate mechanisms that maintain high deep-sea diversity. 322

THE PARADOX OF THE SPECI ES- R I C H D E E P - S E A F L O O R

Theories In the broadest sense, theories on deep-sea diversity may be subdivided into equilibrium and non-equilibrium hypotheses. Equilibrium theories suggest that relatively unchanging conditions create a habitat favourable for high diversity. Sanders’ (1968) stability-time hypothesis is based on the idea that deep-sea environments have been stable and unchanging over evolutionary time, and species have therefore specialised on predictable resources (food, space, etc.) allowing stable coexistence of numerous species with complementary niches. More recently, Sheldon’s (1990) “plus ça change” model, based on trilobite fossil records, hypothesised that physically variable environments promote morphological stasis and generalists, whereas the quiescent nature of the deep sea would promote continuous and gradual evolution of specialised forms. Another equilibrium hypothesis is based on the large area of the deep sea. The species-area relationship that has been observed in many habitats contributed to the idea that the large surface area of the deep sea should facilitate diversiﬁcation because more broadly distributed species are less likely to become extinct over broad scales if refugia exist somewhere in their distribution (Abele & Walters 1979). Productivity has also been identiﬁed as an important factor regulating deep-sea diversity. Given that one of the most striking features of the deep sea is the limited availability of food (Rowe & Gardner 1979), it is reasonable to expect that total availability and pulsing of food might inﬂuence diversity. Available energy has been related to diversity in coral reefs (Fraser & Currie 1996), where a negative correlation between coral generic richness and primary productivity was observed. Van Valen (1976) proposed that low productivity in the deep sea might promote diversity by reducing rates of interaction. For continental shelf gastropods (Roy et al. 1998), higher temperature (and presumably lower productivity) was a signiﬁcant predictor of higher diversity. The relationship between diversity and productivity is the subject of considerable debate, with numerous studies suggesting highest diversity at intermediate levels of productivity (Rosenzweig & Abramsky 1993). Non-equilibrium hypotheses are based on the idea that dynamic processes allow competitively inferior species to coexist in a system with competitive dominants. Small-scale disequilibrium models (e.g. those based on patchy disturbance or varying resources) may yield a dynamic equilibrium on large scales. Thus, one might observe an “equilibrium” fauna for an area showing little change over time, but that might be the product of a mosaic of numerous small-scale disturbances in various stages of post-disturbance succession (Grassle & Sanders 1973). An additional consideration with disequilibrium models is that they often yield similar predictions concerning patterns, namely that some typical spatially/temporally varying process produces patches with varying proportions of competing species. Dayton & Hessler’s (1972) biological disturbance, or “cropping,” hypothesis suggests that predation or disturbance differentially remove competitive dominants, preventing competitive displacement. Grassle & Sanders (1973) proposed the patch dynamic model, in which small-scale patches of food and disturbance create a habitat mosaic that allows species coexistence. This mosaic could encompass temporal and spatial patches of varying quality and composition that species may respond to at different rates and intensities; even if species exploit resources in a very similar manner, random disturbances may reset local species structure before competitive exclusion reduces diversity (Tilman & Pacala 1993). The intermediate disturbance hypothesis or dynamic equilibrium model (Huston 1979) is similar in suggesting that environmental ﬂuctuations prevent attainment of equilibrium, but extremely severe or frequent ﬂuctuations may eliminate sensitive species and thus depress 323

PAUL V. R. SNELGROVE & C R A I G R . S MI T H

diversity. Thus, small-scale patches resulting from moderate disturbance might enhance diversity but chronic organic input would tend to depress diversity. Likewise, feeding pits might enhance diversity but chronic, broad-scale sedimentation events might be expected to have the reverse effect. Jumars et al. (1990) elaborate on this idea to suggest that a competition or predation-induced bottleneck may exist at the larval/juvenile stage in deep-sea populations, and that by specialising on patches or disturbances, where resources are richer or competition is less intense, juveniles may pass through this period of vulnerability. This idea is similar to Holt’s (1993) more general source-sink hypothesis in which highly favourable habitat patches provide a supply of individuals and species to marginal sites where reproduction does not occur. Holt’s model differs in that species moving into “sinks” are living marginally, as opposed to the model of Jumars et al. (1990) where individuals that survive the juvenile bottleneck are able to coexist and reproduce. The relatively low levels of habitat heterogeneity and food resources in the deep sea may not necessarily reduce diversity. A limited resource spectrum may lead to competitive similarity, causing competitive exclusion to occur at relatively slow rates; this can allow infrequent disturbances or stochastic recruitment to reset local species structure before exclusion occurs (Tilman & Pacala 1993). This model may be applicable in the deep-sea benthos as a result of convergent evolution to exploit the thin veneer of recently settled, relatively organic-rich particles at the sediment/water interface, which is consumed by many species of surface deposit feeders. The long generation times, slow population growth rates, and low population densities characteristic of many deep-sea species (Grassle & MorsePorteous 1987, Smith & Hessler 1987, Gage & Tyler 1991) may further extend competitive exclusion times at the deep-sea ﬂoor (see also Grant 2000). In this scenario, one would predict that increases in local diversity would be accompanied by increases in the numbers of ecologically similar species.

Evidence and the theories: broad-scale pattern Several of the above hypotheses lead to explicit predictions on broad-scale pattern. Although several hypotheses are clearly contradicted by some natural pattern, this does not mean that the hypothesis is completely without merit. Indeed, there are several hypotheses that do not explain all patterns but which may partially explain some deep-sea diversity patterns. The stability-time hypothesis predicts a general increase in diversity with depth with abyssal plains being the most diverse communities as a result of their high stability. This prediction may be true in some instances (see Poore & Wilson 1993), but it is inconsistent with the parabolic relationship between depth and diversity in the northwest Atlantic (e.g. Rex 1981). The stability-time hypothesis also predicts increased niche specialisation in the deep sea. So far, no such convincing evidence has been found. A study by Thistle (1983) compared an area subject to intense deep-sea storms with a “stable” area and found comparable harpacticoid diversity, a ﬁnding that is again inconsistent with the stability-time hypothesis. By contrast, macrofaunal diversity was reduced in this (Thistle et al. 1985) and another (Gage et al. 1995) high energy deep-sea area, and patterns in meiofaunal nematodes may also be consistent with this hypothesis (Lambshead et al. 2001a). In the latter study, nematode diversity at one repeatedly disturbed site was reduced relative to a reference site, and the diversity at two other sites historically disturbed by turbidites may have been a function of recovery (recolonisation) time. One ﬁnal issue with respect to this hypothesis is that deep-sea 324

THE PARADOX OF THE SPECI ES- R I C H D E E P - S E A F L O O R

biologists are increasingly impressed with evidence (summarised above) that the deep sea is not as stable as once thought, given the increasing evidence for temporal and spatial heterogeneity. Smith & Kaufmann (1999) recently documented a decrease in carbon ﬂux to the deep eastern North Paciﬁc over a time scale of only 7 yr, which they attributed to changes in surface production (see McGowan et al. 1998) associated with a warming trend. Although changes in community structure over this time period have not been tested, the results from such a comparison are likely to be intriguing. Deep-sea stability over geological time has been questioned by Cronin & Raymo (1997), who argued that major changes in deep-sea community composition have occurred on timescales of thousands to tens of thousands of years, probably as a result of changes in global climate. In terms of the stability-time hypothesis, a critical point is that this instability is thought to have caused transient shifts in deep-sea species distributions rather than extinctions. Despite this new appreciation for variability on multiple temporal and spatial scales, the relative stability of the deep sea must be important to diversity in some context. Many of the other hypotheses summarised above focus on disequilibrium processes operating on some scale, yet paradoxically, processes yielding population disequilibrium are generally more frequent and intense in shallow-water environments. Disequilibrium processes promote diversity through a balance between the rates of formation and disappearance of non-equilibrium conditions. In the deep sea, where rates of population growth, species interaction and physical erasure are low, very modest rates and intensities of disturbance, for example, may balance communities at high levels of diversity. Thus, the seemingly modest sources of disequilibrium in the deep sea may only be important because they are operating in a comparatively stable, low-energy environment. A detritus fall or feeding disturbance may enhance diversity much more in the deep sea than it would in shallow water because its effects will persist much longer and, potentially, it will be used by a broader spectrum of organisms (e.g. allowing ampliﬁed species succession (Smith 1994) ). This point is discussed later (p. 331). There is little doubt that the huge area of the deep sea contributes to the large numbers of species present, but area alone seems inadequate to explain the high local diversity. Without increased numbers of niches associated with increased area, the only factor favouring diversiﬁcation is limited dispersal distances. Barriers to dispersal in the deep sea are not well documented, and it might be expected that, over evolutionary time, some limited subset of species with reduced dispersal ability would still become dominant over broad areas, thus depressing diversity. Given the areal proportions of habitat on a global basis, if area alone were important, then abyssal plains should be most diverse, followed by continental shelves, continental rises, and continental slopes. The majority of patterns described earlier are inconsistent with this prediction. Predator-mediated coexistence is not supported in the broadest sense based on several observations. First, the extended generation times, age-class distributions that are not dominated by juveniles, and slow growth rates are all inconsistent with a system where predation is a major structuring force. It is of course quite possible that predation effects are most intense at larval or juvenile stages, and therefore have little impact on adult population structure (Jumars & Gallagher 1982). If predators increase deep-sea diversity, then one might also expect decreasing diversity with depth because densities of organisms (presumably including predators) decline with depth and attain a minimum on abyssal plains (Sanders et al. 1965). Predation pressure, however, may actually be greater when normalised to population densities and growth rate. Rex (1976) found a signiﬁcant relationship between 325

PAUL V. R. SNELGROVE & C R A I G R . S MI T H

predator diversity and faunal diversity in gastropods, and suggested that productivity entered into the relationship by limiting predator diversity, and thus prey diversity, on abyssal plains. At upper slope depths, the assumption is that pulsing of surface production depresses predator diversity more than in deeper water where pulsing would be dampened. The high abyssal diversity noted by Poore & Wilson (1993) suggests that such an explanation is not easily applied globally. Interestingly, predator exclusion experiments in shallow water suggest that predators depress, rather than enhance, local diversity of sedimentary assemblages (Peterson 1979). Experiments to test the effects of predators have begun in the deep sea (Eckman et al. 2001) but diversity data are not yet available. Given the potential complexity of predator-prey interactions, the role of predators on deep-sea diversity is unlikely to be resolved by analysis of pattern, and will require experimental manipulations. As in all predation experiments, scaling issues are also signiﬁcant. The same problem of generality appears to apply to hypotheses relating productivity to diversity. On a regional scale, Rex et al. (1993) hypothesised that decreased diversity in several taxa with increasing latitude could be attributed to high and variable productivity (though Lambshead et al. (2000) argue the reverse for North Atlantic nematodes). A similar latitudinal trend was noted for demersal ﬁshes off New Zealand (McClatchie et al. 1997); however, the same study found that higher diversity may be linked to higher productivity. Snelgrove et al. (2000) found a signiﬁcant negative correlation between deep-sea gastropod diversity and surface carbon export, although a more complete analysis by Watts et al. (1992), but based on surface chlorophyll concentrations, found that this relationship was weak or non-existent when the factor, depth, was statistically removed. Levin & Gage (1998) reviewed available data on organic matter and oxygen availability from sites in the Atlantic, Paciﬁc and Indian Oceans and found a signiﬁcant negative relationship between sedimentary organic matter and macrofaunal diversity and a signiﬁcant positive relationship between oxygen availability and macrofaunal diversity. But the relationships summarised by Levin & Gage (1998) are undoubtedly inﬂuenced by extreme cases where hypoxia depresses diversity. An unusual situation off the coast of North Carolina provides additional insight into potential interactions between production and diversity. Schaff et al. (1992) found depressed diversity in an area subject to high organic input (and slightly reduced oxygen concentrations) relative to an adjacent area at similar depth with more typical deep-sea conditions. This pattern was also observed for Foraminifera at the same locale (Gooday et al. 2001). Rogers (2000) argues that oxygen minimum zones may actually have acted to increase diversity in adjacent deep-sea systems by permitting habitat specialisation within areas of reduced oxygen and/or areas with increased food ﬂux. Benthic communities within oxygen minimum zones are, nonetheless, themselves relatively low in diversity (Levin et al. 2000). Interestingly, a recent study in the tropical northeast Atlantic found decreased deep-sea polychaete diversity with distance from an upwelling coast onto the abyssal plain, despite the presence of fairly strong currents at the upper slope station (Cosson-Sarradin et al. 1998). Hessler & Jumars (1974) found no relationship between productivity and diversity in the abyssal Paciﬁc, a ﬁnding that was supported by additional data and reanalysis by Paterson et al. (1998). Gooday’s (1996) comparison of Foraminifera from abyssal plain areas in the northeast Atlantic subject to different amounts of phytodetritus input found essentially comparable rarefaction curves at a phytodetritus-rich site compared with the phytodetrituspoor site, with both sites being very diverse. In contrast, increased nematode richness was observed in areas of the equatorial Paciﬁc with enhanced phytodetritus input (Lambshead 326

THE PARADOX OF THE SPECI ES- R I C H D E E P - S E A F L O O R

et al. in press). These varied results suggest that any general relationships between the magnitude and variability of production, and deep-sea diversity, remain to be elucidated. In many ways, disequilibrium processes are more difﬁcult to test as determinants of diversity, particularly by looking at broad-scale pattern. It is unclear at what scale to expect such disturbances to operate and therefore what scale is appropriate for sampling. There are broad patterns that are consistent with disequilibrium hypotheses, but also fail to provide any real test of the ideas. For example, the parabolic relationship between diversity and depth is consistent with an intermediate disturbance hypothesis. It could also, however, be a result of a stable environment favouring species co-existence with low productivity at greater depths constraining the total number of species that can be supported. Thus, the pattern might result from a hybrid of the stability-time hypothesis and the low productivity argument. Rex & Etter (1998) argue that the smaller body size of individuals at upper bathyal depths creates an evolutionary “hotspot” for biodiversity because a favourable balance is achieved between resource availability and body size. On spatial scales of 10–100s of kilometres, deep-sea soft-sediment structure is not homogeneous. Species turn over (or shift from common to rare) with changes in POC ﬂux (Schaff et al. 1992), changes in water depth (Carney et al. 1983) and changes from open slope to canyon or basin habitats (Jumars 1976, Rowe et al. 1982, Grassle & Maciolek 1992, Vetter & Dayton 1998). Etter & Grassle (1992) also documented changes in macrofaunal diversity as sediment diversity changed over similar spatial scales. For macrofaunal species able to disperse 10s to 100s of kilometres, large-scale patchworks could allow source-sink effects to contribute substantially to local diversity. In other words, each patch may harbour a long list of rare, non-reproducing species supplied from other localities; these “trespassers” may coexist for long periods with local “natives” because of competitive similarity and low population growth rates. Limited support for this idea over broad scales is seen in Carney (1997) who compared rare species in a deep-sea environment (the data from Grassle & Maciolek (1992) ) with those along a transect across the Texas continental shelf. He found that rare species at a particular deep-sea station tended to be more common at another deepsea station than was the case for rare species in the shelf environment (i.e., rare species in the deep sea tended to have higher across-site variance in abundances). This pattern is again consistent with the source-sink model wherein some habitats serve as sources for nonreproductive populations in other marginal habitats. The gradual gradients in the deep sea (e.g., downslope changes in temperature and pressure) occurring over broad depth bands may allow such source-sink processes to be particularly important. The idea that competitive similarity may enhance diversity by precluding competitive displacement is supported by Rex & Warén’s (1981) data showing a parabolic relationship between species per genus (S/G) and depth; this S/G pattern corresponded well with the curve for Expected Species with depth. Rex (1983) noted that this pattern might indicate more intense competition at shelf, upper slope and abyssal depths but admits the S/G ratio is only a very indirect measure of competition.

Evidence and the theories: ﬁne-scale pattern and experimentation The strongest evidence that small-scale processes play an important role in deep-sea diversity is, not surprisingly, provided by relatively ﬁne-scale sampling (Tables 2, 3) and experimentation (Table 4). Fine-scale sampling has allowed comparison of species composition within 327

328

0.07–0.5

0.15 0.07– 0.15

∼100 ∼0.10 10–20 m 0.01–0.10

Biogenic pits

Whale skeleton

Subduction of organic matter Burrowing sea urchin furrows

0.10–100 s –

0.07

0.1–5

0.1–1.0

0.1–0.5

Biogenic mounds Bathysiphon sp. tubes

0.2?

Sargassum sp. falls Phytodetrital patches

0.07–1.0

Likely to – range over 0.01–0.5

0.1–0.5

0.5

0.5

Xenophyophore tests

0.1–0.5

0.1

Polychaete mudballs Sponge spicules

∼0.25

?

Rare

Common in Bathysiphon zone up to 7

0.2–1.0

Likely high in algal bloom Areas

?

0–18

∼0.01 in Catalina Basin

?

Scale of Spatial sampling frequency (no. m−2) (m)

Spatial scale of effect (m)

Source

Kukert & Smith 1992, C. R.

Grassle & MorsePorteous 1987 Rice et al. 1986, Thiel et al. 1988/9, Hecker 1990, Rice & Lambshead 1994, Smith et al. 1996

Levin et al. 1986

Jumars 1975, Levin & Edesa 1997 Jumars & Eckman 1983, Rice & Lambshead 1994, Bett & Rice 1992

Source

Table 2 Natural sources of small-scale patchiness in macrofaunal community structure at the soft-sediment deep-sea ﬂoor, and the nature and scales of their effects. “Enhanced diversity” means an increase in diversity as measured by rarefaction. (NB: the documented spatial scales of patchiness are often limited by the scale(s) of sampling).

PAUL V. R. SNELGROVE & CRAIG R. SMITH

329

NE Paciﬁc Margin Positive correlation between (1050 m) harpacticoid species and polychaete functional groups NE Paciﬁc Margin Reduced abundance of some (1240 m) common foraminiferan species*, but not others NE Atlantic slope Enhanced abundance of some (1350 m) and foraminiferan species, increased abyss (5000 m) nematode patchiness NE Atlantic Slope Foraminiferans correlated with depth in sediment, different species

0.10–0.5

Seasonally high

0.2–1.0

0.07–0.50 High

0.20

Predation

Predation refuge, special food resources,

Hypothesised mechanisms

Thistle et al. 1993

Thistle 1988, Thistle & Eckman 1990

Thistle 1983

Thistle 1979b

Thistle 1979a

References

Biological interactions

Enhanced food availability

Lambshead & Gooday 1990, Rice & Lambshead 1994 Gooday 1986

Burial disturbance Levin et al. 1991

Escape from predation, enhanced microbial food Many possible

Locally high ?

0.01–0.10 0.01–0.10 Ubiquitous

0.10

0.2–0.3

0.07

*Foraminiferans were actually macrofaunal in size but are included here for consistency

Vertical partitioning

Phytodetrital accumulations

Natural and artiﬁcial mounds

Polychaete functional groups

0.5–1000

100– 10 000

10–100

10–100

Spatial frequency (no. m−2)

0.07–0.50 10–50

0.10

0.10

NE Paciﬁc Margin Harpacticoid abundance negatively (1220 m) correlated with surface deposit feeders Erosive currents NW Atlantic slope Diversity remains high despite (removal of (4625 m) removal of small-scale biogenic biogenic strutures) structures Worm mudball NE Paciﬁc Margin Enhanced abundance of one (1035–1050 m) harpacticoid species near live mudballs 0.07

0.10–100

0.10

NE Paciﬁc Margin Harpacticoid species abundances (1220 m) correlated with different structures

Spatial Scale of scale of sampling effect (m) (m)

Biogenic structures (worm tubes, Foraminifera balls, etc.) Surface deposit feeders

Community effects

Site(s)

Source

Table 3 Natural sources of small-scale patchiness in meiofaunal community structure in the soft-sediment, deep-sea ﬂoor, and the nature, scales and frequency of their effects in the speciﬁc site(s) studied. “Enhanced diversity” means an increase in diversity as measured by the rarefaction method. (NB: the documented spatial scales of patchiness often are limited by the scale(s) of sampling).

THE PARADOX OF THE SPECIES-RICH DEEP-SEA FLOOR

330 Rare to common

0.1–0.5

0.1–0.5

NE Paciﬁc Margin (1240 m), NE Atlantic Slope (2120– 4150), St Croix (900 m)

Enriched sediment trays and depressions

Up to 1

0.20

0.2–0.3

NE Paciﬁc Margin (1240 m)

Artiﬁcial mounds

Rare

0.15–2.5

NE Paciﬁc Margin (1300 m), NW Atlantic Slope (3600 m)

Nekton falls

1–2

? common on very small scale

0.10–0.5

0.10–0.5

Colonisation by unusual and common species, diversity < background community

NW Atlantic Slope (1880, 3600 m), NE Atlantic Slope (2120– 4150), NE Paciﬁc (1240), St Croix Fieberling Guyot (585, 635)

Unenriched, azoic sediments in trays

Reduced abundance and diversity, increased abundance of a few common and rare species Enhanced diversity, reduced community abundance Reduced diversity, different rare species attracted to different types of enrichment

Rare

0.15–5

0.3–0.6

Increased abundance of rare (opportunistic) species

NW Atlantic Slope (1630 and 3506 m), Bahamas (2066 m)

Wood islands

Spatial frequency of analogues (no. m−2)

Community effects

Site(s)

Source

Scale of Spatial sampling scale of effect (m) (m)

Open substratum, increased food availability from dead infauna, bedload transport from adjacent sediment

Sediment scour, organic enrichment

Burial disturbance

Specialised food availability, larval habitat selection?

>1 yr?

>2 yr >2 yr

Organic enrichment, provision of specialised food (wood)

>2–3 yr

0.25–5 + yr

Hypothesised mechanisms

Residence time of effects

Desbruyères et al. 1980 Levin & Smith 1984 Desbruyères et al. 1985 Snelgrove et al. 1992 Snelgrove et al. 1994 Snelgrove et al. 1996

Kukert & Smith 1992

Turner 1973, Turner 1977, Grassle & MorsePorteous 1987 Grassle 1977, Desbruyères et al. 1980, Levin & Smith 1984, Desbruyères et al. 1985 Grassle & MorsePorteous 1987, Snelgrove et al. 1992, Snelgrove et al. 1994, Snelgrove et al. 1996, Levin & DiBacco 1995 Grassle & MorsePorteous 1987 Smith 1986

References

Table 4 Experimental patches, with close natural analogues, in macrofaunal community structure on small spatial scales in the soft-sediment, deep-sea ﬂoor, and the nature and scales of their effects. “Enhanced diversity” means an increase in diversity as measured by the rarefaction method. (NB: the documented spatial scales of patchiness often are limited by the scale(s) of sampling).

PAUL V. R. SNELGROVE & CRAIG R. SMITH

THE PARADOX OF THE SPECI ES- R I C H D E E P - S E A F L O O R

different patch types. Experimentation has attempted to mimic different types of patches under more controlled conditions than is possible through sampling of natural patches. Unfortunately, the predictions and expectations from ﬁne-scale sampling and experimentation are often ambiguous because there are multiple mechanisms by which heterogeneity can enhance diversity. For example, a given patch type may actually exhibit enhanced diversity (e.g. have more species) relative to adjacent areas. Thus, the patch itself is a site of increased diversity and thereby enhances local diversity. A second (not mutually exclusive) possibility is that different suites of species are supported within different patch types. Such a scenario would enhance regional diversity if the patches were fairly frequent within the habitat and/or the patches provided a source of colonists for adjacent areas (e.g. via source-sink processes). Grassle & Grassle (1992) differentiate between organic input as a food source (reviewed by Gooday & Turley 1990), and physical disturbances that reduce possible competition but provide no new food resources. Manipulative experiments using defaunated sediments and faecal mounds fall largely into the latter category, but most other manipulative experiments have focused on organic matter as food. Sampling of natural pertubations has also focussed on food-related resources, with some studies addressing disturbances that create space rather than provide food resources (Tables 2, 3). Natural analogues of non-enrichment disturbance that operate on multiple scales include intense currents, slumping events and predation. On small scales (2 cm to metres), the abundance of particular benthic species or guilds is often correlated (positively or negatively) with the natural occurrence of particular biogenic structures, such as pits, worm mudballs, mounds, foraminiferan tubes, or xenophyophore tests. Such patterns have been observed for macrofauna (e.g. Levin et al. 1986) and meiofauna (e.g. Thistle & Eckman 1990), and are summarised in Table 2. While these correlations are consistent with habitat partitioning and a patch mosaic, they are generally quite weak, and apply to only a very small subset of the hundreds of species at a particular site. In most cases, faunal composition is remarkably homogeneous from patch to patch within a site in terms of species presence, relative abundances, and diversity, despite changes in small-scale biogenic structures (Jumars 1976, Smith 1986, Grassle & Maciolek 1992, Kukert & Smith 1992, Schaff et al. 1992, Schaff & Levin 1994). Indeed, Jumars (1976) found little evidence for pattern in deep-sea benthos at various scales and suggested that processes operating at the scale of the organism might be the most relevant. He further suggested that until ambit sizes of organisms are known, the sampling scales appropriate for detecting patterns are difﬁcult to predict. In summary, biogenic patchiness on the scales studied to date may contribute to, but in themselves, are inadequate to explain the extremely high local levels of species diversity in the deep sea. Although species composition in patches may be different from surrounding sediments, the number of species involved is usually modest. Experimental studies on patchiness have focused on organic matter (Turner 1973, Smith 1986, Grassle & Morse-Porteous 1987, Snelgrove et al. 1992, 1994, 1996), defaunation (Grassle 1977, Desbruyères et al. 1980, Desbruyères et al. 1985, Levin & Smith 1984, Snelgrove et al. 1992, 1994, 1996, Levin & DiBacco 1995), and physical disturbance (Smith et al. 1986, Kukert & Smith 1992). The general conclusion that may be drawn from these macrofaunal studies is that fauna responding to different patch types did so in different relative abundance and (usually) different species composition from the surrounding sediments. It is clear, given the strength of response in many systems and the marked contrast with the surrounding community, that some species are capable of habitat selection. In most instances only a single patch type was tested within a given environment, but there is evidence that different patch types will elicit different responses and that some species select 331

PAUL V. R. SNELGROVE & C R A I G R . S MI T H

particular patch types (Snelgrove et al. 1992, 1994, 1996). However, once again the numbers of species responding are very small compared with regional species lists, and comparative data from other areas and patch types are needed to test the generality of this result. Only three processes (burial disturbance, bioturbation around a whale fall, and the presence of xenophyophore tests) of the many studied caused enhanced local diversity relative to surrounding sediments (Tables 2, 3). Of the remaining processes for which effects on diversity were addressed, most, such as patchy enrichment from wood, diatom detritus or Sargassum, yield reductions (usually) or no change in diversity. If these remaining processes are enhancing within-patch (alpha) diversity in the deep sea, they must be doing so over longer timescales than those studied (which are limited to months to several years in the deep sea). Because two of the three diversity-enhancing patches involve disturbance (i.e., population reductions), these results are consistent with the intermediate disturbance and patch mosaic hypotheses. The impact of xenophyophores on local diversity seems most likely to operate via habitat partitioning. Organic enrichment (e.g. Sargassum sp., wood, diatom detritus, and dead ﬁsh) often causes dramatic population enhancement of “opportunistic” species (e.g. capitellid polychaetes, leptostracans and cumaceans; Turner 1977, Smith 1986, Grassle & Morse-Porteous 1987, Snelgrove et al. 1994). By supporting different suites of species than are found in ambient sediments, these small-scale enrichment events are clearly contributing to diversity. The species collected from some patches are rare or absent from the background community (Smith 1986, Grassle & Morse-Porteous 1987, Snelgrove et al. 1994), and as such may not contribute signiﬁcantly to local diversity over most of the seaﬂoor (noting, however, that rare species are a signiﬁcant component of deep-sea diversity). One open question is whether there are a sufﬁcient number of different patch types (which could vary in the type of organic input, or its stage of decomposition) to support specialised faunas and to yield regional enhancement of diversity. Few patch types have been sampled and there are insufﬁcient data to suggest that patch specialisation alone can account for much of the diversity in deep-sea systems. Thus far, both random and targeted studies of patchiness in the deep sea have generally provided little indication of a large number of intense patches harbouring characteristic faunas. Whether this is because specialisation on patch types is relatively unimportant, or because many of the patch-related processes occur at such low frequencies that they are difﬁcult to detect through modest sampling efforts, is unclear. The deep-sea infauna is very poorly sampled (the largest area sampled at any site being only 21 m2), with the result that patches occurring at low frequencies are likely to have remained undetected. Moreover, even where a speciﬁc patch type might be of critical importance to a given species, if only a few individuals respond at a given time (because of low densities or stochastic processes) then detecting such an effect would be very difﬁcult. More studies focused on sampling, and experimenting with, speciﬁc potential patch types (e.g. algal falls, bioturbation features, different phytodetritus accumulations, sponge-spicule mats) are badly needed to elucidate this issue (cf. Smith et al. 1986, 1998, Grassle & Maciolek 1992, Levin et al. 1986, Snelgrove et al. 1992, 1994, 1996).

What data do we need to understand regulation of deep-sea diversity? To a limited extent, some of the same data that are needed to improve on diversity estimates would also help improve on our understanding of diversity regulation in the deep sea. 332

THE PARADOX OF THE SPECI ES- R I C H D E E P - S E A F L O O R

Generalisations are often refuted by conﬂicting data from areas that are poorly sampled (e.g. South Paciﬁc), or because a different taxonomic group was examined. In the latter case, there is some evidence that meiofaunal taxa may be less impacted by physical disturbance than macrofaunal groups. Given the differences in the lifestyles and dispersal potential between macrofauna and meiofauna, it is quite conceivable that processes regulating diversity differ between the groups. It is for this reason that there is a pressing need for studies that are more taxonomically comprehensive within a single locale. When such studies are paired with improved models and data on carbon export (Falkowski et al. 1998) and surface production (Behrenfeld & Falkowski 1997), they will help to clarify the enigmatic relationships between production, depth, latitude, and deep-sea diversity. Aside from better spatial and taxonomic coverage, repeated access to the deep-sea environment is critical to evaluate the importance of habitat heterogeneity. Not only is improved access needed to sample more patch types, but better time-series coverage is needed. For most experimental patches, data exist from only one or two time points, and for natural patches the time axis is poorly explored. In summary, we need better sampling of episodic events from a variety of areas around the world.

Conclusions It is clear that there is no single process that is responsible for the high diversity of deep-sea ecosystems. As is increasingly evident in a variety of high-diversity systems (e.g. coral-reef assemblages, Ormond & Roberts 1997), a variety of non-equilibrium processes combine to enhance species diversity over a range of scales. We believe there is a host of nonequilibrium processes operating in relatively stable, low-productivity deep-sea environments that create conditions favouring development and maintenance of high diversity. Nonetheless, there is considerably more known now about pattern and regulation of deep-sea diversity than when Sanders & Hessler ﬁrst made their important discoveries. At the very least, it can be said that considerable variability in diversity is observed in deep-sea systems, and some areas such as the South and equatorial Paciﬁc may be particularly species rich in at least some categories (isopods and nematodes, respectively). Although there is considerable debate regarding total metazoan species numbers in the deep sea, it is clear that the deep sea harbours a signiﬁcant component of the metazoan global species pool. A similar conclusion could quite conceivably be reached for microbes. Many deep-sea areas are characterised by low dominance and large numbers of species, and the authors are convinced that, for the (mostly North Atlantic) areas where comparisons have been made, alpha diversity in deepsea areas is higher than in shallow areas. The high alpha diversity in the deep sea appears to result from an array of disequilibrium processes, including disturbance, succession, and response to different types of seaﬂoor patchiness. Competitive similarity and source-sink effects also appear likely to contribute to high local diversity. On regional scales, existing data indicate higher within-habitat diversity in deep-sea than in most shelf habitats that have been sampled, although the existing database is woefully poor. Considerably more data are needed to elucidate the relative contributions of shelf and deep-sea communities to the oceanic species pool. It is also apparent that deep-sea areas subjected to strong environmental forcing, such as intense currents or oxygen stress, are likely to have reduced macrofaunal diversity. There is no simple depth-diversity relationship but many examples suggest that peak diversity is observed at intermediate slope depths, assuming that strong environmental 333

PAUL V. R. SNELGROVE & C R A I G R . S MI T H

forcing (e.g. the presence of an oxygen minimum zone) does not play a role. There is no simple relationship between productivity and deep-sea diversity, but it is hypothesised that if highly productive areas and environments with overriding environmental (e.g. high currents, hypoxia) or geological (e.g. isolation, anoxia) controls are excluded, a relationship between amount and variability of production and diversity may emerge (e.g. Lambshead et al. in press). This speculative example illustrates an emerging pattern in deep-sea ecological syntheses; multiple factors appear to be important and no single process or characteristic regulates deep-sea diversity. The implication is that confounding variables, such as depth or total productivity, must be removed statistically or experimentally in order to evaluate other potentially important variables.

Deep-sea diversity and why it matters Despite the many inherent difﬁculties in evaluating diversity patterns and species richness in deep-sea ecosystems, there is clearly great interest in producing accurate diversity estimates and in understanding processes (both evolutionary and ecological) that regulate diversity in the deep sea. From a pragmatic perspective, there are concerns about the potential impacts that future deep-ocean waste dumping and mining might have on biodiversity. The impacts of anthropogenic global climate change are also of concern because past climatic changes appear to have inﬂuenced deep-sea diversity over geological timescales (Cronin & Raymo 1997) and because ongoing climate change may already be inﬂuencing carbon ﬂux to deepsea communities (Smith & Kaufmann 1999, see also Smith et al. 2000). However, the desire and need to understand diversity in the oceanic sea-bed environment goes well beyond immediate conservation concerns. The deep sea is the Earth’s “last unexplored frontier”; it offers a habitat that appears tantalisingly simple in physical structure and temporal variability, yet presents a logistical nightmare in accessibility and amenability to experimental manipulation. It also continues to be the most enigmatic of the Earth’s high diversity ecosystems; until we can convincingly explain the causes of high biodiversity in the seemingly monotonous deep-sea muds, our ecological and evolutionary theories must be considered inadequate. Any understanding of global biodiversity regulation must include a basic understanding of the Earth’s most extensive habitat in area. Despite its unique size, homogeneity, and openness, it is likely that any lessons learned regarding regulation of deep-sea diversity will be very useful in the management of other ecosystems. The deep sea offers one of the few remaining habitats on Earth largely unaltered by human activity. As such, it may be the one of a small number of suitable “laboratories” in which we can evaluate regulation of biodiversity in a truly natural ecosystem.

Acknowledgements This work grew initially from a workshop (Deep-Sea Biodiversity: Pattern and Scale) led by M. A. Rex and conducted at the National Center for Ecological Analysis and Synthesis, a Center funded by NSF (Grant #DEB-94-21535), the University of California/Santa Barbara, 334

THE PARADOX OF THE SPECI ES- R I C H D E E P - S E A F L O O R

and the State of California. We are grateful to H. Caswell, R. J. Etter, E. D. Gallagher, J. F. Grassle, R. R. Hessler, M. A. Rex, and C. T. Stuart for many insightful discussions during the workshop. J. F. Grassle, J. S. Gray and P. J. D. Lambshead and many others provided helpful perspectives that helped clarify our thoughts when we presented this work at a Linnean Society of London/British Natural History Museum Symposium (Marine Biodiversity) hosted in 1997 by P. J. D. Lambshead and B. Sherwood. We thank J. P. Grassle and J. F. Grassle for very insightful comments on an earlier draft of this manuscript, and P. J. D. Lambshead for his helpful review. This paper is contribution No. 5984 from SOEST, University of Hawaii at Manoa.

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STATUS AND MANAGEMENT OF WORLD SEA URCHIN FISHERIES N. L. ANDREW 1 , Y. AGATSUMA 2 , E. BALLESTEROS 3 , A. G. BAZHIN 4 , E. P. CREASER 5 , D. K. A. BARNES 6 , L. W. BOTSFORD 7 , A. BRADBURY 8 , A. CAMPBELL 9 , J. D. DIXON 1 0 , S. EINARSSON 11 , P. K. GERRING 1 , K. HEBERT 1 2 , M. HUNTER 5 , S. B. HUR 13 , C. R. JOHNSON 14 , M. A. JUINIO-MEÑEZ 1 5 , P. KALVASS 16 , R. J. MILLER 17 , C. A. MORENO 1 8 , J. S. PALLEIRO 1 9 , D. RIVAS 20 , S. M. L. ROBINSON 2 1 , S. C. SCHROETER 1 0 , R. S. STENECK 22 , R. L. VADAS 23 , D. A. WOODBY 2 4 AND Z. XIAOQI 2 5 1 National Institute of Water and Atmospheric Research, P.O. Box 14-901, Kilbirnie, Wellington, New Zealand email: [email protected] 2 Laboratory of Applied Aquatic Botany, Graduate School of Agricultural Science, Tohoku University, Tsutsumidori-Amamiya 1-1, Aoba, Sendai, Miyagi 981-8555, Japan 3 Centre d’Estudis Avançats de Blanes-CSIC, E-17300 Blanes, Girona, Spain 4 Kamchatka Research Institute of Fisheries and Oceanography, Naberezhnaya 18, Petropavlovsk-Kamchatsky 683002, Russia 5 Maine Department of Marine Resources, West Boothbay Harbor, ME 04575 USA 6 Department of Zoology, University College Cork, Lee Maltings, Cork, Ireland 7 Department of Wildlife, Fish and Conservation Biology, University of California, Davis, CA 95616, USA 8 Washington Department of Fish and Wildlife, Point Whitney Shellﬁsh Laboratory, Brinnon, WA 98320, USA 9 Fisheries and Oceans Canada, Paciﬁc Biological Station, Nanaimo, BC, Canada V9R 5K6 10 Marine Science Institute, University of California, Santa Barbara, CA 93106, USA 11 Marine Research Institute, P.O. Box 1390, Skúlagata 4, 121 Reykjavík, Iceland 12 Alaska Department of Fish and Game, P.O. Box 667, Petersburg, AK 99833, USA 13 Department of Aquaculture, Institute of Fisheries Science, Pukyong National University, 714 U 1-dong, Haewundae, Pusan, 612-021, South Korea 14 Tasmanian Aquaculture and Fisheries Institute, University of Tasmania, GPO Box 252-05, Hobart, TAS 7001, Australia 15 Marine Science Institute, University of the Philippines, Diliman, Quezon City, Philippines 1101 16 California Department of Fish and Game, 19160 South Harbor Drive, Fort Bragg, CA 95437, USA 17 Fisheries and Oceans Canada, Dartmouth, Nova Scotia, Canada B2Y 4A2 18 Instituto de Ecologia y Evolucion, Universidad Austral de Chile, Casilla 567, Valdivia, Chile 19 Calle 16 No. 25, Centro, Ensenada, Baja California, Mexico, C.P. 22880

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Departamento de Pesquerías, Subsecretaria de Pesca, Bellavista 168, Piso 17, Casilla 100-V, Valparaiso, Chile 21 Fisheries and Oceans Canada, St Andrews, NB, Canada E5B 2L9 22 Darling Marine Center, University of Maine, Walpole, ME 04573, USA 23 Department of Biological Sciences, University of Maine, Orono, ME 04469, USA 24 Alaska Department of Fish and Game, PO Box 240020, Douglas, AK 99824, USA 25 Fisheries College, Ocean University of Qingdao, 5 Yushan Road, Qingdao, China 266003

Abstract World production of sea urchins peaked in 1995, when 120 306 t were landed. Chile dominates world production, producing more than half the world’s total landings of 90 257 t in 1998. Other important ﬁsheries are found in Japan, Maine, British Columbia, California, South Korea, New Brunswick, Russia, Mexico, Alaska, Nova Scotia, and in a number of countries that produced less than 1000 t in 1998. Aside from the Chilean ﬁshery for Loxechinus albus, most harvest is of Strongylocentrotus spp., particularly S. intermedius, S. franciscanus, and S. droebachiensis. Only a small minority of ﬁsheries have been formally assessed and in the absence of such assessments it is difﬁcult to determine whether ﬁsheries are over-ﬁshed or whether the large declines observed in many represent the “ﬁsh down” of accumulated biomass. Nevertheless, those in Chile, Japan, Maine, California and Washington and a number of smaller ﬁsheries, have declined considerably since their peaks and are likely to be over-ﬁshed. Fisheries in Japan, South Korea and the Philippines have been enhanced by reseeding hatchery-reared juveniles and by modifying reefs to increase their structural complexity and to promote the growth of algae. Sea urchin ﬁsheries have potentially large ecological effects, usually mediated through increases in the abundance and biomass of large brown algae. Although such effects may have important consequences for management of these and related ﬁsheries, only in Nova Scotia, South Korea and Japan is ecological knowledge incorporated into management.

Introduction Most sea urchin ﬁsheries are found in the temperate regions of the world and are concentrated on only a handful of genera. The harvested product of these ﬁsheries is the gonad of both sexes (more usually and loosely termed “roe”). There are long traditions of consuming sea urchin roe in many cultures, particularly in Asia, Polynesia, in the Mediterranean, and in Chile. In addition to the many small artisanal and domestic commercial ﬁsheries, modern commercial ﬁsheries are now focused on the Japanese market which consumes more than 80% of the world’s production (Kawamura 1993, Sonu 1995, Hagen 1996). Sea urchin roe is a premium food in Japan where it is eaten raw as sashimi, served with rice as sushi (“Uni Don” in Japanese), or preserved in small bottles mixed in brine or alcohol and salt (“Shio uni”, “Tsubu uni” and “Ita uni”). Particularly well known are the “Echizen Uni” brand, processed since the 1600s in Fukui (Taki & Higashida 1964), and “Shimonoseki Uni” which have been sold in Yamaguchi since the 1800s (Kan 1968). A baked casserole of roe served on the shells of the Japanese surf clam (Pseudocardium sybillae) or ezo-abalone (Haliotis discus hannai) is called “Kaiyaki Uni” and remains a popular regional dish. Most sea urchin roe imported to Japan arrives fresh or frozen in various stages of processing from bulk packaged only to ﬁnished trays of uni (Table 1). Importation of fresh or frozen roe 344

345

Total

USA Canada North Korea South Korea China Chile Hong Kong Russia Mexico Australia New Zealand Philippines Norway Taiwan Indonesia Peru Vietnam Palau Iceland

Country

46.9 0.6 0.6

15.3 0.1 0.1

20 297

5.3 5.2

1.6 1.2

3997

11 435.4 2 089.7 248.1 1 291.3 1 746.3 3 163.0 238.4 6.1 – 20.7

2034.0 445.1 63.6 229.8 381.7 786.9 31.9 1.7 – 3.4

1526 2795

45.3

– 3.5

– 1.1

19.5

31.3 66.5 94.8 9.1 2.1 2542.8

51.5 83.3 63.2 1.7 2.1 1304.0

Value

Amount

Amount

Value

Sea urchin only Frozen roe

Sea urchin only Chilled roe

371

12.8

164.2 181.5 10.4 1.1 0.3 0.5 – 0.4

Amount

1114

9.4

217.1 852.0 30.0 0.5 2.2 2.4 – 0.9

Value

Sea urchin only Salted or brined roe

7077

0.04

10.0

4672

0.5

7.8

2577.4 –

10.2

7.5

4376.3 –

870.4 418.2 787.3

Value

961.2 466.8 1255.0

Amount

Sea urchin only Whole animals

729

0.2

5.0

1.1 –

16.1 673.9

32.3

Amount

1303

1.4

7.5

2.7 –

33.9 1194.6

62.6

Value

Sea urchin & holothurians Processed roe

Table 1 Amount (t) and value (million Yen) of sea urchins and sea urchin products imported to Japan in 1999. Note that data for “processed roe” includes holothurians. Dash indicates data not available. Data source: Japan Tariff Association. STATUS AND MANAGEMENT OF SEA URCHIN FISHERIES

N. L. ANDREW , Y. AGATSUM A, E. BALLE S T E R O S , A . G . B A Z H I N , E T A L .

increased from 2643 t in 1988 to 3367 t in 1992 and 5523 t in 1999 (Table 1, Sonu 1995, Hagen 1996, Keesing & Hall 1998). Most of this roe came from the USA, Chile, South Korea and Canada. In contrast, imports of salted or brined roe, mostly from the Korean peninsula and China, decreased from 1248 t in 1988 to 371 t in 1999 (Table 1). Whole sea urchins are also imported from the USA, Canada and Russia; in 1999 7077 t of whole sea urchin were imported. France is the world’s second largest consumer of sea urchin roe, consuming around 1000 t per year (Hagen 1996). There are large domestic markets in many sea urchin producing countries, notably Chile, New Zealand and the Philippines. In this review we provide an overview of the current status and management of the world’s sea urchin ﬁsheries. Summaries of trends in world production are followed by synopses of major ﬁsheries, presented in declining order of production in 1998 (see Table 2). The level of detail is based on the relative importance of the ﬁshery, whether lessons may be learnt from its management and the quantity of citable research done. Following these summaries, stock enhancement in sea urchin ﬁsheries is summarised, and ﬁnally we make some general observations about the ecological effects of sea urchin ﬁsheries and their management. This review is designed to complement the comprehensive reviews of the ecology of edible sea urchins provided in Lawrence (2001). Unless noted, the term “recruitment” is used as it is in the ecological literature to mean “entry into the observable benthic population”. In the ﬁsheries literature it usually refers to “entry to the ﬁshery”; where we use the term in this context we use qualiﬁers to make that usage clear. We use the term “barrens habitat” to describe areas of reefs in which sea urchins occur at high density and which are grazed clear of large brown algae. We use the term in preference to “isoyake” because that term more speciﬁcally describes the reduced ﬁsheries production that comes from areas of barrens habitat (Taniguchi 1996). All weights are reported as whole animal wet weight unless speciﬁed otherwise and sizes are given as test diameter (TD in mm). The acronym MLS refers to Minimum Legal Size, MxLS refers to Maximum Legal Size, and TAC refers to annual Total Allowable Catch.

World production World production is difﬁcult to estimate accurately because FAO statistics (FAO 2000a,b) are reported for all echinoderms combined; in some countries, particularly those in the tropics, there are signiﬁcant holothurian ﬁsheries (Sloan 1985, Conand & Byrne 1993, Dalzell et al. 1996, Conand 2001). To estimate sea urchin production, we started with countries listed by FAO as having some echinoderm production, plus Australia, and from that list estimated catch as follows: (a) wherever possible estimates of catches were obtained directly from scientists and managers working on each ﬁshery (see author list), (b) based on Conand’s (2001) estimates of sea cucumber production between 1986 and 1996, countries in the tropics with signiﬁcant catches of holothurians (Ecuador, Indonesia, Kenya, Madagascar, Malaysia, Maldives, Mozambique, New Caledonia, Palau, Papua New Guinea, Solomon Islands, Sri Lanka, Tanzania, Tonga, Vanuatu, and Yemen) were compared with the FAO statistics and excluded because the holothurians accounted for the echinoderm harvest, (c) Denmark was excluded because Sloan (1985) reported that a ﬁshery for Asterias rubens was active in the 1970s and 1980s and there have been no reported landing of echinoderms since 346

S TAT US AND M ANAGEM ENT OF S E A U R C H I N F I S H E R I E S

1984 (FAO 2000a), (d) Catch estimates for France were taken from Le Direac’h (1987) for the period 1954–84 and FAO thereafter, and (e) FAO catch estimates for echinoderms were used for China, Fiji, North Korea, Peru, Russia (and the former USSR), and Spain. Catch statistics are reported for the period completely covered by FAO statistics (1963– 98), and more extensive time series are reported where available. The estimates of harvest based on FAO statistics almost certainly underestimate true landings, particularly prior to the 1980s. Furthermore, excluded countries may have some sea urchin production, there may be under-reporting, and the FAO list may be incomplete. Estimated harvests may include some catches of asteroids in those countries for which speciﬁc records of sea urchin landings are not available. World production of sea urchins steadily increased though the latter half of last century to a peak of 120 306 t in 1995 (Fig. 1, Table 2). Since 1995, total production has declined quickly and in 1998 was only 75% of its peak 3 yr earlier (Fig. 1, Table 2). Underlying the overall increase in production before 1995 was a series of expansions and declines of regional or national ﬁsheries, particularly in Japan, the USA and Chile (see summaries below). Following an explosive development in the 1990s, Chile now accounts for more than half the world’s production. If Chile is excluded, world production has been declining for the last 10 yr. In 1998, a total of 90 257 t was landed in the world’s sea urchin ﬁsheries. Traditionally, sea urchins in the northern hemisphere genus Strongylocentrotus have accounted for most of the harvest (Fig. 2) but their contribution has steadily declined since 1990 and made up only 39% of the world’s production in 1998. This decline was greatest in ﬁsheries for S. franciscanus, S. droebachiensis and S. intermedius. After explosive growth of the Chilean ﬁshery for Loxechinus albus, this species now dominates world production – in the decade from 1988, 382 161 t were harvested. Two species, Strongylocentrotus franciscanus and S. droebachiensis, are widely distributed in North America and Europe (see Scheibling & Hatcher 2001, Tegner 2001 for recent reviews) and are much sought after by the Japanese 140

Total catch (x 1000 t)

120 100 80 60 40 20

1965

Figure 1

1970

1975

1980 Year

1985

1990

1995

Total world sea urchin production (t) between 1961 and 1998.

347

Loxechinus albus All species Strongylocentrotus droebachiensis S. franciscanus S. droebachiensis S. franciscanus S. franciscanus S. purpuratus S. droebachiensis All species S. franciscanus S. droebachiensis All species S. droebachiensis Tripneustes gratilla Evechinus chloroticus Paracentrotus lividus Tripneustes gratilla S. franciscanus S. droebachiensis All species S. franciscanus All species All species L. albus All species P. lividus S. droebachiensis Anthocidaris crassispina

Chile Japan Maine (USA) British Columbia (Canada)

348

Total

China Oregon (USA) Australia North Korea Peru France Ireland Iceland Taiwan

South Korea Nova Scotia (Canada) Philippines New Zealand Spain Fiji Washington (USA)

New Brunswick (Canada) Russia Alaska (USA)

California (USA) Baja California (Mexico)

Species

Country/Fishery

90 257

44 843 13 653 7 688 6 088 182 4 782 1 429 412 1 621 1 590 1 419 <9 1 410 1 299 974 716 558 503 228 92 239 157 112 100 90 59 4 0 0

1998 Production (t)

120 306

54 609 13 735 16 845 6 807 88 10 097 2 720 468 1 658 2 344 949 19 3 707 1 021 584 975 0 894 339 173 150 701 78 140 131 78 10 923 63

1995 Production (t) 54 609 27 528 17 821 13 499 1 019 23 582 8 493 814 1 900 6 328 2 921 100 7 751 1 325 1 120 1 032 595 1 771 4 024 464 300 4 222 255 252 461 987 352 1 409 300

Peak production (t) 1995 1969 1993 1992 1992 1988 1986 1997 1996 1986 1997 1988 1986 1996 1992 1993 1997 1988 1988 1988 1967 1990 1992 1987 1996 1970 1976 1994 1967

Year of peak production

Table 2 Total production (t) by country/ﬁshery and species sorted in declining order of production in 1998 (the last year of complete records), and for 1995 (peak of world production) and tonnage for the year of the peak production. See text for details of ﬁsheries with more than one species. In those instances in which the ﬁshing year differs from the calendar year, the reported year refers to the calendar year in which the ﬁshing year ﬁnishes. N. L. ANDREW, Y. AGATSUMA, E. BALLESTEROS, A. G. BAZHIN, ET AL.

STATUS AND MANAGEMENT OF SEA URCHIN FISHERIES

70

Total catch (x 1000 t)

60

S. franciscanus S. droebachiensis All Strongylocentrotus

50 40 30 20 10

1975

Figure 2

1980

1985 Year

1990

1995

Total world production of Strongylocentrotus spp. (line) and by species (bars).

market. S. franciscanus is distributed along the western seaboard of North America from Baja California to Alaska and is harvested throughout its range. Catches have declined since peaking at about 35 000 t in 1989 to less than half that now (Fig. 2). Fisheries for S. droebachiensis are concentrated in Maine and the Maritime Provinces of Canada, but smaller ﬁsheries exist in Alaska, British Columbia, Washington and Iceland. Commercial harvesting, almost exclusively for the Japanese market, expanded rapidly in the late 1980s and peaked in 1993 at approximately 22 454 t (Fig. 2). Since then catches have fallen markedly, and in 1999 were 39% of those in 1993. In his review of echinoderm ﬁsheries in 1985, Sloan noted that “. . . S. droebachiensis is not the subject of an appreciable ﬁshery.” The rise of this species as a contributor to world production has been rapid and, although its contribution has declined, it remains an important species.

Chile The ﬁshery The ﬁshery for the endemic sea urchin Loxechinus albus is the largest in the world. L. albus are relatively slow growing and may live to be as old as 20 yr and as large as 130 mm (Stotz et al. 1992, Zuleta & Moreno 1994, Gebauer & Moreno 1995, see Vásques 2001 for review). L. albus feed mainly on drifting algal material (Castilla & Moreno 1984, Moreno & Vega 1988) and are found in intertidal pools on rocky shores and on subtidal reefs to a maximum depth of 15 m (Vásques et al. 1984). In common with many invertebrate ﬁsheries, the development of the Chilean sea urchin ﬁshery may be divided into three phases (Moreno & Zuleta 1996), existing initially as a small ﬁshery, followed by a phase of rapid expansion, and then a third phase of full exploitation 349

N. L. ANDREW , Y. AGATSUM A, E. BALLE S T E R O S , A . G . B A Z H I N , E T A L .

37°

Concepcion

X

Chiloe Island

XI

200 km

Puerto Natales

Punta Arenas

XII 56°

Figure 3 Map of southern Chile showing boundaries of Regions X to XII and important locations.

and probable decline. In the ﬁrst phase, before 1975, <3000 t yr−1 was caught and sold as fresh roe for the domestic market. Chile is divided into 12 regions, and the most important sea urchin ﬁsheries are in the three most southern regions (X–XII, Fig. 3). Catches were initially concentrated in the south in Region X (Fig. 4) and were taken mostly from the intertidal zone. The only regulation in place during this phase was a MLS of 100 mm, introduced in 1974. The ﬁshery entered a decade of rapid expansion in 1976 in response to economic policies that promoted exports. Catches grew by 2800 t yr−1 as markets were developed in Asia (principally Japan) for frozen roe and preserved, dry-salted and dehydrated products. The ﬁshery changed markedly as large numbers of new entrants switched from exhausted benthic ﬁsheries in central and northern Chile and diving with surface-supplied air became the predominant method. Historically, benthic ﬁsheries in Region X, including those for sea urchins, loco (the muricid snail Concolepas concolepas), luga (Iridaea spp.), and bivalves such as huepo (Ensis macha) and culengue (Gari solida), were concentrated in the northern parts of the 350

S TAT US AND M ANAGEM ENT OF S E A U R C H I N F I S H E R I E S

Proportion of total catch

1 Region X

0.8 0.6 0.4

Regions I to IX

0.2 0 Region XII

1965

1970

1975

1980

1985

1990

1995

2000

Year

Figure 4

Catch in Regions I–IX, X, and XII as a proportion of total harvest in Chile.

Guaitecas Archipelago and Guafo Island. The main landing port was Quellón, at the end of the Pan-American highway on the southern tip of Chiloé Island. As these resources became depleted, ﬁshing effort moved further south and production from Region X remained high, masking a large-scale decline in the resource. Eventually these areas too were over-ﬁshed and the ﬂeet was driven south into Patagonia by falling catches. Processing facilities were established in Punta Arenas (see Fig. 3) and production from Region XII expanded a great deal, driven part by increased proﬁtability of harvesting in remote areas. Sea urchins are now harvested from the full length of the Chilean coastline from the border with Peru to Cape Horn – the ﬁshery is now well into its third phase of development, one of full exploitation and probable decline.

Production Although small compared with what it was to become, the Chilean sea urchin ﬁshery in 1976 was one of the world’s largest. In the late 1970s and early 1980s the ﬁshery expanded rapidly and peaked in 1995 when Chile produced 54 609 t of sea urchins, 45% of the world’s production (Fig. 5). Since then the ﬁshery has declined to 44 843 t in 1998 but this still represented half the world’s production in that year (Table 2). Before the expansion of the ﬁshery in the 1980s, most harvest came from Region X (>90% between 1965 and 1999; Fig. 4). Region IX has produced only small quantities (22 t in 35 yr of ﬁshing) and Region XII produced an average of only 138 t yr−1 before 1993. Landings in Region X peaked in 1985 at 30 261 t, and have slowly but erratically declined since then (Fig. 4). Between 1965 and 1999, Regions I to IX accounted for less than 4% of the total harvest. Signiﬁcant harvests were recorded in Regions I to IV in the 1990s but these were never more than 3050 t per annum and have subsequently declined (Fig. 4). As catches from the southern regions have increased substantially, the signiﬁcance of Regions I to IX has further declined. 351

N. L. ANDREW , Y. AGATSUM A, E. BALLE S T E R O S , A . G . B A Z H I N , E T A L .

60

Total catch (x 1000 t)

50 40 30 20 10

1965

1970

1975

1980

1985

1990

1995

Year

Figure 5

Total catch (t) of sea urchins in Chile.

Driven by declining catches in Regions X and XI, the ﬁshery rapidly expanded into the most southern region, XII, in the mid-1990s (Fig. 4). Catches rose from 287 t in 1992 to 26 998 t 3 yr later. In 1999, 7848 registered divers worked in Region XII. As a result of this movement of ﬁshing effort, the proportional contribution of Region X to the national harvest declined signiﬁcantly (Fig. 4). In 1999, this trend was reversed for the ﬁrst time in eight years but it remains to be seen whether this signals a return to previously ﬁshed regions. Within Region XII, ﬁshing has radiated out from landing points such as Puerto Natales and Punta Areñas to the more exposed margins of the archipelagos and channels and, by 1999, ﬁshing had extended to the furthermost reaches of the region.

Management Management of coastal ﬁsheries in Chile spans the range of management regimes and effectiveness. North of Valdivia, on the exposed coast, the “caleta” system of small-scale co-management has successfully conserved and managed artisanal ﬁsheries for valuable species, including sea urchins. This system grew out of the poor state of Chile’s benthic ﬁsheries in the 1980s (Bustamante & Castilla 1987), recoveries of exploited populations in two small marine reserves in central Chile (Moreno et al. 1984, 1987, Duran & Castilla 1989) and the success of similar management seen in Japanese coastal ﬁsheries. The 1991 Fisheries Law provided a legal basis for local syndicates of ﬁshers to claim exclusive ﬁshing rights in areas of coastline out to 5 nautical miles from shore. This system transformed the fortunes of near-shore ﬁsheries (Castilla 1994, 2000, see Castilla et al. 1998 for review). Sea urchin ﬁshing in these “Areas of Management and Exploitation” is based on a plan that requires ﬁshers to make six-monthly projections of stock status that are used by the Under-secretary of Fisheries to set a quota for the area. The Fishers’ Union then decides how that quota is caught. In December 1999, 184 areas were managed under this system, 352

S TAT US AND M ANAGEM ENT OF S E A U R C H I N F I S H E R I E S

covering a total of 432 ha of near-shore reef. Of these, 57% contained sea urchins, as well as other sedentary species. Unfortunately, the total area managed this way represents a small fraction of the 73 223 ha requested by ﬁshers, and the estimated 4 533 442 ha available along the coast (Montecinos 2000). In June 2000, there were only 14 caletas in Regions X to XII. In sharp contrast to this situation, most of the Chilean sea urchin ﬁshery occurs without any effective management. With the large number of artisanal ﬁshers in the sea urchin and other benthic ﬁsheries, there remain few restrictions on ﬁshing for most of the exposed coastline and the more complex coast south of Chiloé Island. There have, nevertheless, been several national management initiatives. These include: 1) 2) 3)

4)

Closure of Regions I and III from 1983 to 1987. Creation of a National Register of Fishermen in 1995 and establishment of a moratorium on new entrants to benthic ﬁsheries at that time. Summer closures during the spawning season. The spawning season varies along the long coast of Chile. In the extreme north Loxechinus albus reproduce in the austral spring–summer and possibly autumn, while in the south spawning appears restricted to spring (Gutiérrez & Otsu 1975, Bückle et al. 1978, Bay-Schmith et al. 1981, Zegers et al. 1983, Zamora & Stotz 1992). Introduction of a MLS of 100 mm in 1974, subsequently revised in 1980 and 1986 following estimates of fecundity and the size at maturity. L. albus matures between 40 mm and 60 mm (Guisado 1995). A 70-mm MLS, although introduced at the request of ﬁshers has been widely ignored since its introduction; between 1995 and 1998, 27% of the landed catch was less than the MLS.

The explosive development of the ﬁshery in the south was largely unregulated. The introduction of ﬁshery regulations on a national level (e.g. MLS and diver registration) have not constrained ﬁshing effort or catch. The absence of reliable indices of abundance or ﬁshery-derived data means that there is little information that can be used to improve management. Most important, there is no information on the replenishment of populations after ﬁshing nor on the ecological effects of removing such a large biomass of herbivores. Although the area-based management developed for the central and northern regions of the ﬁshery allows sustainable use there, it is inappropriate south of Chiloé Island, where the ﬁshery is now concentrated. New and innovative management will be required to ensure the future of this ﬁshery.

Japan The Fishery Sea urchin tests have been excavated from middens in Japan from the Jomon and Yayoi Neolithic periods, extending from at least 8000 bc to ad 200. Sea urchins or “gaze” were ﬁrst recorded as foods in ad 833 in the “Ryonogike”, an annotated edition of “Yoryo ritsuryo”, a law enforced in ad 757 (Kawamura 1969). According to Kinoshita (1955), commercial sea urchin ﬁsheries in Hokkaido began on the coast of the Sea of Japan between 1877 and 1886 but landings remained small for the next 50 yr. In 1932, the Rishiri Fisheries Co-operative Association in northern Hokkaido began buying sea urchins to 353

N. L. ANDREW , Y. AGATSUM A, E. BALLE S T E R O S , A . G . B A Z H I N , E T A L .

Table 3 Species harvested and catch (t) in 1997 by prefecture. Abbreviations are: Si = Strongylocentrotus intermedius, Sn = S. nudus, Hp = Hemicentrotus pulcherrimus, Pd = Pseudocentrotus depressus, Ac = Anthocidaris crassispina and Tg = Tripneustes gratilla. Prefecture

Catch (t)

Species

Hokkaido Nagasaki Iwate Miyagi Aomori Kagoshima Yamaguchi Miyazaki Kumamoto Fukuoka Hyogo Ehime Saga Tokushima Oita Shimane

6541 1287 1205 1013 718 446 441 231 221 219 214 187 162 138 123 99

Si, Sn Hp, Pd, Si, Sn Sn Si, Sn Hp, Pd, Hp, Pd, Ac Hp, Pd, Hp, Pd, Pd, Ac Hp, Pd, Hp, Pd, Hp, Pd, Hp, Pd, Hp, Pd,

Ac

Ac Ac Ac Ac Ac Ac Ac Ac Ac

Prefecture

Catch (t)

Species

Okinawa Fukushima Kochi Tottori Akita Fukui Kyoto Mie Wakayama Ishikawa Chiba Niigata Yamagata Ibaragi Kanagawa Aichi

87 81 54 50 24 21 20 18 14 9 7 7 7 3 3 2

Tg Sn Ac Hp, Pd, Ac Sn Hp, Ac Hp, Pd, Ac Hp, Pd Pd, Ac Ac Pd Sn, Pd Sn Sn Pd Pd

protect the ﬁshery for the kelp Laminaria ochotensis and roe processing began the following year. Landings increased during the 1930s to about 1000 t and increased further following World War II as the ﬁshery expanded into new areas. Similar increases in production occurred in other prefectures and Japan’s sea urchin ﬁsheries developed to become the largest in the world prior to 1985. Sloan (1985) reviews much of the early literature on the Japanese sea urchin ﬁshery. There are no recreational ﬁsheries for sea urchins in Japan. Six species: Strongylocentrotus intermedius, S. nudus, Hemicentrotus pulcherrimus, Pseudocentrotus depressus, Anthocidaris crassispina and Tripneustes gratilla account for most of commercial landings in Japan (Table 3). A further nine species are consumed but catches are small and restricted to local areas (Kawamura 1993). Sea urchins are harvested by diving, wading, scoop nets, hooks, and spears. In western Honshu and Kyushu, women divers (known as “ama”) collect sea urchins using breath-hold diving; in other regions where there are substrata with very little relief, nets, beam trawls and other indirect methods are used. On the Hidaka coast, Strongylocentrotus intermedius is harvested from the intertidal zone using a net which entangles sea urchins as it is dragged along the bottom. In deeper waters (>7 m) off the coast of Aomori, S. nudus is harvested using traps baited with algae. The Japanese market prefers ﬁrm roe so all ﬁsheries are closed during the spawning season. These closures differ in their timing and duration and are based on an extensive understanding of the reproductive biology of harvested species (Fig. 6). For example, S. intermedius in Hokkaido spawns in the Sea of Japan in autumn, in Funka Bay in spring and autumn and in the Okhotsk Sea and the Paciﬁc Ocean, over a long period from spring to autumn (Fig. 7). These differences have been attributed to genetic differences between populations (Agatsuma 2001a). Fishing along the coast of the Japan Sea, and western Tsugaru 354

S TAT US AND M ANAGEM ENT OF S E A U R C H I N F I S H E R I E S

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov Dec

Strongylocentrotus intermedius

Soya Strait, Sea of Japan, Western Tsugaru Strait Central Tsugaru Strait Eastern Tsugaru Strait – Southern Pacific Ocean Funka Bay Hidaka Eastern Pacific Ocean Nemuro Strait

Strongylocentrotus nudus

Sea of Okhotsk Soya Strait, Sea of Japan Western Tsugaru Strait Central Tsugaru Strait Eastern Tsugaru Strait – Southern Pacific Ocean Funka Bay Hidaka Figure 6

Fishing seasons (dark bars) and season closures for sea urchin ﬁshing in Hokkaido.

Strait is restricted to May–August. At other localities in Hokkaido, harvesting is during winter to spring (Agatsuma 2001a). The spawning season of S. nudus appears to be less dependent on oceanographic conditions and occurs during September–October throughout Hokkaido (see Agatsuma 2001b for review). Fishing is restricted to July–August except in the Tsugaru Strait and the Paciﬁc Ocean where roe recovery is rapid (possibly because of abundant large brown algae) and the ﬁshing season is closed until spring (Fig. 6). S. nudus are caught in April to August in Aomori, Iwate and Miyagi Prefectures (Kawamura 1993). Fishing seasons for Hemicentrotus pulcherrimus, Pseudocentrotus depressus and Anthcidaris crassispina are similarly based on the annual reproductive cycles (Nakamura & Yoshinaga 1962, Kawamura 1993, Agatsuma 2001c). 355

N. L. ANDREW , Y. AGATSUM A, E. BALLE S T E R O S , A . G . B A Z H I N , E T A L .

Sea of Okhotsk Soya Strait Nemuro Strait

Hokkaido

0

150

Hidaka Funka Bay Tsugaru Strait

300 km

Aomori Iwate

Sea of Japan

Miyagi Fukushima

Honshu Pacific Ocean Fukui Hyogo

Shimane Wakayama Tokushima

Yamaguchi Fukuoka Saga Nagasaki

Ehime

Shikoku

Oita Miyazaki Kumamoto

Kyushu

Kagoshima

Pacific Ocean East China Sea

Figure 7 Map of Japan showing the major islands and most important sea urchin-producing prefectures.

Production Total landings in Japan increased from 7200 t in 1953 to a peak of 27 528 t in 1969 then catches ﬂuctuated between 23 000 t and 27 000 t until 1987 when landings began to decline (Fig. 8). Catches during the 1990s ﬂuctuated between 13 000 t and 15 000 t. Sea urchin 356

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ﬁsheries are concentrated in Hokkaido, in northern prefectures facing the Paciﬁc Ocean on Honshu, and to a lesser extent on Kyushu in southern Japan (Fig. 7, Table 3). Hokkaido accounted for 48% of the total landings in 1997 (Table 3). Catch statistics are generally not available by species but some reconstruction is possible given the disjunct distributions of several species. Strongylocentrotus intermedius is harvested from Hokkaido and the Paciﬁc coast prefectures of Aomori and Iwate, while ﬁsheries for S. nudus extend further south to Ibaragi on the Paciﬁc coast and Yamagata prefecture in the Sea of Japan (see Agatsuma 2001a,b for reviews). All of the landings from Hokkaido and the northern prefectures of Aomori, Akita, Iwate, Miyagi, and Fukushima are Strongylocentrotus spp. and together these accounted for 70% of national landings in 1997. Catches from prefectures on southern Honshu, Shikoku and Kyushu comprise a mixture of species: Hemicentrotus pulcherrimus, Pseudocentrotus depressus and Anthocidaris crassispina predominate. Tripneustes gratilla is landed only in Okinawa where 87 t (6.3% of national production) was landed in 1997. Landings in the southern prefectures have nearly halved since 1985 (Fig. 9). Catches in Hokkaido began to decline in the mid-1980s, fell sharply between 1988 and 1991 (Fig. 10), then stabilised and increased in the latter half of the 1990s. Most of this harvest was Strongylocentrotus intermedius and the declines (in roe weight) were principally from this species (Fig. 10). Harvests of S. nudus roe followed a similar but less marked decline and recovery during this period. With the decline in landings of S. intermedius in Hokkaido (Fig. 10), S. nudus is now the most important species in Japan: the ratio of S. intermedius to S. nudus has changed from 4 : 1 in 1966 (Kawamura 1969) to 1 : 1 or less since 1990. S. nudus is the only species harvested in Miyagi and Fukushima. Landings in Miyagi have slowly declined over the last 20 yr and 1013 t whole weight was landed in 1998 – approximately 40% of that caught in 1982 (Fig. 11). Catches in Fukushima are considerably smaller and there is no consistent trend in landings in the last 20 yr (Fig. 11). 357

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Management Current management of coastal ﬁsheries in Japan may be traced to the 1948 Fisheries Cooperative Association Law and the Fisheries Law of 1949 (see Ruddle 1987 for an English language summary). These laws amended and extended a 1901 statute that provided exclusive ﬁshing rights to associations of ﬁshers, which in turn was derived from the system of village guilds that managed ﬁshing rights and privileges bestowed by feudal lords in earlier times (Ruddle 1987, Lim et al. 1995). The Fisheries Law cedes a property right to prescribed 358

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Figure 11

Total catch (t) of Strongylocentrotus nudus in Miyagi and Fukushima prefectures.

areas of sea bed to Fisheries Co-operative Associations (FCAs) which in turn distribute ﬁshing rights to individuals, such as “territories”, daily catch limits, and the timing and duration of ﬁshing trips. In addition to ownership of these resources (there is no legal distinction between tenure over land or sea ﬂoor), a web of interdependencies (“mutual help”) and codes of conduct binds ﬁshers and communities together (Ruddle 1987, Lim et al. 1995). Fishing rights are not tradable in a western sense but can be inherited or transferred in other ways although generalisations are impossible given the complexity of local customs and laws (Ruddle 1987). The 1974 Coastal Fishing Ground Improvement and Development Law provides the basis for stock enhancement in Japan and is pivotal in developing management policy (see pp. 393–397). Government has considerable involvement in the management of coastal ﬁsheries, particularly in providing subsidies for enhancement and infrastructure development and has an overall responsibility for co-ordinating management among Associations. The most important means for government involvement are “Sea Area Fisheries Adjustment Commissions” (SAFAC) which are composed of the relevant FCAs within a prefecture and two tiers of government – municipal and prefectural (Ruddle 1987). For each prefecture (or management area in Hokkaido), a plan for ﬁshing within each “sea area” is developed, and includes resource management guidelines and policies such as MLSs, closed areas and seasons, method restrictions and so on. It is the responsibility of the Prefectural Fisheries Agency to establish those plans, which are interpreted and implemented by each FCA. In Hokkaido, for example, the prescribed MLS for S. nudus and S. intermedius have been increased from 40 mm and 50 mm, respectively, in 49% and 25% of FCAs. Similarly, in Hokkaido, about one half the FCAs impose a daily catch limit on members for harvesting S. intermedius and 44% limit the daily harvest of S. nudus. Most FCAs restrict ﬁshing to 2–5 h day−1 (73% and 91% of FCAs limit ﬁshing for the two species, respectively). TACs are much less popular and are imposed by less than 10% of FCAs. Because the FCAs manage all ﬁshery resources within their sea area, management of sea urchins is integrated with that of other resources, such as seaweeds and abalone, and FCAs will often intervene to “manage” ecological relationships (e.g. crabs and sea stars are removed from reefs in 40% of FCAs). 359

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The persistence of Japan’s sea urchin ﬁsheries under the current management regime for more than ﬁfty years attests to both the durability of the resource and the efﬁcacy of management. However, production has declined in the last 20 yr and in several of the more important prefectures. Japan is increasingly reliant on imports to satisfy its huge demand. Japan was overtaken as the world’s largest producer of sea urchins in the mid-1980s by Chile in 1985 and USA in 1987 (it regained its rank behind Chile in 1998 because of sharp declines in the Maine ﬁshery). Much of the reduction in catches stems from declines in the S. intermedius ﬁshery in Hokkaido and that for S. nudus in Miyagi. Despite these long-term declines, no formal assessments of the status of stocks in any region or prefecture have been reported in the scientiﬁc literature. It is difﬁcult to ﬁnd evidence in the published literature of catch or effort restrictions in the face of declining catches. Enhancement is one of the most important management tools used to conserve and rebuild Japanese sea urchin ﬁsheries (see pp. 393–397).

Maine (USA) Sea urchins have been ﬁshed in Maine since at least 1929. Before the 1970s, catches were shipped to major cities on the eastern seaboard to supply the domestic market and catches remained less than 100 t. The ﬁshery began in earnest in 1987 to supply processed roe to Japan and quickly became the world’s largest ﬁshery for S. droebachiensis. The ﬁshery grew rapidly from 1987 to a peak of 17 821 t in 1992–93, then just as rapidly declined to less than a third of this size in 1999–2000 (Fig. 12). The ﬁshery is divided into two zones for

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management purposes: southwest (Zone 1) and the northeast (Zone 2), divided at western Penobscot Bay. Zone 1 was the more important of the two for the ﬁrst eight years of the ﬁshery; the northeast zone now accounts for 61% of total catch (Fig. 13). Between 1994 and 1999, mean catch rate of divers fell by 13%, despite a 62% decline in catch (Fig. 12). Mean catch rates have fallen the most in Zone 2, but have also declined consistently in Cumberland County in Zone 1. Although mean catch rates in all counties in Zone 1 recorded in 1999–2000 are lower than those in 1994–95, they have ﬂuctuated considerably among years. In part, the designation of two zones was based on the reproductive and spawning cycle along the coast of Maine. Spawning can begin as early as February–March in the southwest and as late as May–June in the northeast. Typically, the major spawning periods are March– April and April–May, respectively, for the two zones (Vadas et al. 1997, Vadas & Beal 1999). Following spawning there is a recovery of the gonad index to a baseline level (about 5%), which is maintained throughout the summer. Recovery to harvestable levels (about 10%) begins in early fall and accelerates in late fall. Until regulations were imposed, harvesting occurred from early August to June and essentially ignored the biological cycle of the species. Divers using SCUBA catch approximately 80% of the total harvest, most of the remainder is caught using drags, and a very small amount (13 t in 1999–2000) is caught using rakes in the intertidal zone. In 1999–2000 there were 339 draggers in the ﬁshery, working mostly in the northeast of the State where the large Bay of Fundy tides make diving difﬁcult. The average tows lasts 7 to 9 min, typically in water between 5 m and 20 m deep. Four types of drags are commonly used, the most primitive of which, the “chain sweep”, is a modiﬁed scallop dredge (Creaser & Weeks 1998, Wahle 1999). Derivatives of this design have been developed speciﬁcally for sea urchins and are of lighter construction. Recently, dragging has accounted for a greater proportion of the total catch, increasing from 16.7% in 1997 to 20.6% in 2000, reﬂecting a shift in the ﬁshery from the southwest to the northeast. 361

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Management In its early years, the Maine sea urchin resource was considered inexhaustible and the ﬁshery developed rapidly, unfettered by management until 1992. Growing concerns about over-harvesting in that year prompted legislation and regulations that limited access to the ﬁshery to licensed harvesters and restricted their methods. This constraint did little to limit ﬁshing effort and the number of licensed harvesters increased rapidly, from 1075 in 1992 to 2725 in 1994. A perceived decline in the stock prompted more legislation and Department of Marine Resources regulations in the following years. Management of this ﬁshery is based on restricting ﬁshing effort; no limits are placed on individual or total catch. After the introduction of licenses failed to reduce ﬁshing effort, a three-year moratorium on the issue of new permits was introduced in 1995. The moratorium was then replaced with a limited entry scheme that requires ﬁve licenses to be retired for each new entrant. In 2000, 1040 ﬁshers were licensed to harvest sea urchins in Maine. Other regulations included the restriction of drag width, banning night-time ﬁshing, the creation, in 1994, of two zones that limit ﬁsher’s mobility, ﬁshing seasons, and a MLS of 2 in (51 mm) introduced in 1993. In 2000–01, a MxLS of 3.5 in (89 mm) was introduced, then reduced to 3 in (77 mm) shortly thereafter. Six small areas, each extending along approximately 300 m of coastline were closed to ﬁshing in 1999 to provide unﬁshed reference sites for research. There are no marine reserves or other protected areas in Maine. An Industry Council was established in 1996 to advise on ﬁshing seasons and its role was expanded in 1997 to consider all ﬁshery-related issues. Virtually all new management initiatives have come from this Council. The ﬁshery in 1999–2000 was less than a third of the peak size. Trends in catch and effort perceived by the Industry Council and from preliminary analyses by the Department of Marine Resources provide evidence that the resource is in decline. In the absence of a formal stock assessment it is difﬁcult to determine whether the current, diminished catches are sustainable or whether the decline in catch represents the “ﬁshing down” of accumulated biomass. Reliance on ﬁshery-derived information such as catch rates is risky as they are difﬁcult to interpret. There have been many ﬁshery-independent surveys of sea urchins in Maine (see Steneck 1997 for review), but their coverage is uneven and results have not been used in an assessment of the ﬁshery. In autumn 1999, harvesters reported an extensive mortality of legal-sized sea urchins in shallow water along the western end of the northeast zone (Zone 2). This event coincided with abnormally high water temperatures and the bloom of a common non-toxic dinoﬂagellate. A lesser mortality was reported in the autumn of 2000, east of the previous year’s event, also in a year of unusually warm water. Its appearance was similar to those observed in Nova Scotia, but tests for Paramoeba invadens, the causative organism there, were negative (M. Hunter unpubl. data). The cause, areal extent and effect of the mortality on the ﬁshery remain poorly understood. Another potential industry concern is the presence of two growth forms of sea urchins in Maine. In one of two intensively studied populations, a slow growth morph has been identiﬁed growing sympatrically with the normally faster growing form in shallow subtidal waters (Vadas et al. 2001). The fast growing form lives 16–20 yr and grows to the MLS in 4–6 yr, whereas the slow morph lives 8–12 yr and does not attain legal size. Both forms reproduce and therefore can be acted on by selection. Continued harvesting could select against the

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fast morph. The distribution and abundance patterns of the slow morph are unknown at present. The wealth of information available on the ecology of shallow subtidal reefs in the Gulf of Maine, and the biology of important species there, provide a good basis for improved management of the Maine ﬁshery. Reﬁnements to the current management regime, such as further effort reductions and changes to the seasons and MLS (Vadas & Beal 1999) may offer some prospect of improving the ﬁshery but given the apparent large-scale changes in the ecology of reefs in the Gulf of Maine more radical approaches may be required. Co-management of the lobster and sea urchin ﬁsheries by maintaining a mosaic of kelp forests and barrens habitats (Steneck 1997) may offer a better prospect of sustaining the sea urchin ﬁshery. Such an approach to management would require the active co-operation of the ﬂeets and closure of signiﬁcant areas of coastline. In any event, a formal stock assessment, using all available information, would provide the necessary catalyst and foundation for change.

British Columbia (Canada) Three species of sea urchin have been harvested in British Columbia – the ﬁrst and most important is the red sea urchin, Strongylocentrotus franciscanus. A ﬁshery for the green sea urchin S. droebachiensis, began in 1987. Small catches of the purple sea urchin S. purpuratus were recorded in the early 1990s but there is now no ﬁshery for this species. Commercial harvesting for all species has always been by diving only. There are both First Nation and recreational harvests for red and green sea urchins. Each year, 2% of the province-wide TAC for red sea urchins is allocated to First Nations for social, food and ceremonial purposes. For management, the coastline of British Columbia is divided in two at Cape Caution, just north of Vancouver Island. The North Coast extends from Cape Caution to Alaska and the South Coast from Cape Caution to the border with Washington.

Strongylocentrotus franciscanus Red sea urchins were ﬁrst harvested in the 1970s (Breen 1979, Campbell & Harbo 1991, Campbell et al. 1999). The ﬁshery remained small (<500 t) until 1983, then catches steadily increased until the early 1990s when catches doubled (Fig. 14). Initially, the ﬁshery operated inside Vancouver Island but in 1986 catches were recorded on the North Coast. Landings peaked in 1992 when 13 499 t was landed, 85% from the North Coast. In subsequent years catches have slowly declined, in large part because of the introduction and subsequent reductions of TACs. The ﬁshery was unrestricted until 1991 when participation was limited and nearly half the divers were removed from the ﬁshery. In the late 1980s regulation became more complex and that used on the North and South coasts diverged. On the South Coast annual catch was limited by a largely arbitrary TAC, divided among small areas. Fishing was restricted to four days per week. On the North Coast an experimental rotational ﬁshing regime was implemented, but after large catches in 1992 this was supplemented with an arbitrary competitive TAC. The over- and under-catching of the TAC that was a problem through much of the 1990s has diminished as the industry matured and assessments improved. Rotational ﬁshing was

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retained in both regions and reﬁned as knowledge of the resource grew. The two regions were formalised in 1994 and divers were required to choose between them. The province-wide TAC in 1996 was then equally apportioned among the 110 license holders remaining in the ﬁshery, resulting in 81% of the catch being allocated to the North Coast and the remainder to the South Coast. A 100-mm MLS was introduced in 1987 to allow sea urchins to spawn 3–6 times before entering the ﬁshery. In 2000, a trial reduction in MLS to 90 mm was implemented because of a market preference for 90 to 120-mm sea urchins, accompanied by a 12% cut in the TAC as a precautionary measure. A MxLS of 140 mm was implemented in 1988 but abandoned in 1993.

Strongylocentrotus droebachiensis In 1987, green sea urchins (S. droebachiensis) were also harvested (Harbo & Hobbs 1990). Although catches of this species were much smaller than for the red sea urchin, they also peaked in 1992 at 1019 t (DFO 1999, Perry & Waddell 1999). Annual landings then declined to only 88 t in 1995; after introduction of a TAC they stabilised at around 160 t. Poor and inconsistent roe quality has hampered development of the green sea urchin ﬁshery (Perry & Waddell 1999). Green sea urchins larger than the MLS of 55 mm are harvested during winter from the South Coast. Green sea urchins from the north of the province have had poor roe and in 1997 the North Coast was closed to ﬁshing except under exploratory permit. Since 1995, the total harvest has been capped by a single TAC. In 1999 the assessments were reﬁned and the ﬁshery is now managed as four stocks based on known patterns of water circulation and the larval biology of S. droebachiensis (Perry & Waddell 1998, Perry et al. 1998). Of the three South Coast stocks, only two – inside Vancouver Island and Juan de Fuca Strait – are important and model-based TAC advice is provided for these. There is negligible ﬁshing for green sea urchins on the exposed west coast of Vancouver Island. Within each stock the annual TAC is apportioned among 3–5 areas based on the previous year’s catch and non-transferable quotas are equally divided among divers within each area. 364

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Some management areas in the northern Strait of Georgia, between the two centres of ﬁshing effort, are closed to ﬁshing (Perry & Waddell 1999).

Assessment and status TACs for both species are estimated using deterministic surplus production methods (Campbell et al. 1999, Perry & Waddell 1999). The assessments differ in the way biomass is estimated. For red sea urchins, Campbell et al. (1999) apply Gulland’s (1971, from Schaefer (1954) ) approximation MSY = XMBo, but substitute current biomass for pre-ﬁshing biomass (Bo) and use a “scaling factor” of X = 0.2 across a range of natural mortalities (M) between 0.05 and 0.15. Current biomass is estimated as the product of densities and the area ﬁshed. Densities are estimated using stratiﬁed random surveys in beds known from logbook records. Different survey methods have been used in the almost 30 yr sea urchin populations have been surveyed (Campbell et al. 1999); to address the uncertainty this introduces, the lower 90% conﬁdence limit of the biomass estimate is used in TAC advice. The estimates of ﬁshing area are assumed to be made without error, despite high uncertainty (Campbell et al. 1999). Recommended TACs for S. franciscanus for the North and South Coasts for 2000 were 4024 t and 844 t, respectively. Green sea urchins have been surveyed only in a subset of areas within the assessed stocks (Waddell et al. 1997) and the data are not used to estimate biomass (Perry & Waddell 1999). Current biomass is estimated within a surplus production model (Schnute 1977, Polovina 1989) ﬁtted to catch rates from the ﬁshery. Information from the survey is used to provide independent estimates of catchability coefﬁcients and biomass for the two areas surveyed (using the method described for red sea urchins). As for red sea urchins, the estimated MSY for each region is scaled as a precaution against the determinism of model dynamics and uncertainty in catch rates. Auxiliary information from surveys and from the literature is used to reﬁne the recommended TAC within this range, in both cases towards the lower half of the range (Perry & Waddell 1999). The 1999 assessment indicated that the stock centred on Queen Charlotte Strait was healthy but the status of the southern stock was both worse and more uncertain (Perry & Waddell 1999).

California (USA) The ﬁshery The sea urchin ﬁshery in California is dominated by the red sea urchin, S. franciscanus. Small catches (14 t in 1999) of the purple sea urchin, S. purpuratus, are landed but are less than 1% of landings in California and will not be considered further. The commercial ﬁshery for red sea urchins began in southern California in the early 1970s. It remained south of Point Conception until 1985 when it expanded rapidly north of Bodega Bay (see Kato & Schroeter 1985, Kalvass & Hendrix 1997 for detailed reviews). Sea otters (Enhydra lutris) are now abundant along the coast between Point Conception and Bodega Bay and there are few harvestable sea urchins in this region (Ebert 1968, Estes & Palmisano 1974, Estes & Duggins 1995). The California ﬁshery is exclusively a dive ﬁshery. Typically, divers work alone or in pairs using surface supplied air and a line tender. In the northern region divers 365

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almost always make day-trips but in southern California the ﬁshery is concentrated on the Channel Islands and trips may last several days. Recreational and customary ﬁsheries for sea urchins in California are negligible and illegal harvesting is believed to be small (Kalvass & Hendrix 1997). In northern California, sea urchins are harvested from a relatively narrow strip of subtidal reef from the intertidal zone to about 22 m deep. Broadly, this distribution coincides with that of the bull kelp Nereocystis luetkeana and is estimated to be approximately 14.5 km2 in area (McLean 1962, Van Wagenen 1989). In southern California, an analogous minimum estimate can be made using the canopy coverage of Macrocystis pyrifera, which covers approximately 45 km2 along the coast between Point Arguello and the Mexican border, and the Channel Islands (Van Wagenen 1989). The rapid growth of the ﬁshery in the latter half of the 1980s was partially due to a strengthening of the Japanese Yen against the US dollar in 1986 and the consequent increase in ex-vessel prices. In 1999, the commercial ﬁshery was estimated to be worth US$14.4 million. The catch is exported to Japan as either fresh or frozen roe, depending on quality. Roe recovery is greatest during autumn and winter. Prices are generally highest in the Japanese markets in December and May because roe quality and demand are high and Japanese domestic and imported roe supplies during winter months are relatively low (Reynolds 1994, Sonu 1995). Roe harvested from southern California commands a higher price than from the north.

Production Catches steadily increased in southern California from the early 1970s to a peak in 1981 of 11 213 t (Figs 15 and 16), but declined in 1982–83. This decline has been attributed to the

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El Niño of 1982–83 when warm water weakened or killed kelp (Kato & Schroeter 1985). In the mid-1980s there were large increases in total catches to a peak in 1988 of 23 582 t, much of the increase coming from the developing ﬁshery in northern California. At its peak, in 1988, 13 846 t of sea urchins were landed in the northern region. Since 1988, the total catch has declined by 73% to approximately 6409 t in 1999 (Fig. 15). Much of this decline occurred in the northern region (in 1999, 1445 t was landed there – 11% of the 1988 catch). The biggest drop in catch and effort in California occurred after the 1992 season because many divers returned to southern California following the 5–7 yr ﬁshing down of unexploited northern stocks (Kalvass & Hendrix 1997). 367

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Although the Californian ﬁshery ranges over a wide area, effort has focused on small “hot spots” that change through time. Before the development of the northern region, and after its equally rapid decline, the northern Channel Islands (San Miguel, Santa Rosa and Santa Cruz) have provided the greatest share of the catch. In 1999, the northern Channel Islands contributed 2640 t of the 4964 t southern California catch (53%), down 44% from the 4714 t contribution in 1994. In northern California, 70% of the 50 800 t of red sea urchin harvested between 1988 and 1994 came from about 65 km of coastline (Kalvass & Hendrix 1997). Divers have also moved from shallow (<10 m) water to working reefs in deeper water. In northern California catch-by-depth changed little between 1988 and 1993, then in 1995 there was a marked shift to deeper (>10 m) waters (Kalvass & Hendrix 1997). In southern California the percentage of catch from depths greater than 21 m increased from 7% in 1990 to 12% in 1999. Interpreting these changes in ﬁshing behaviour is difﬁcult because they probably have many causes, including differences in weather (both short-term and longer-term cycles such as ENSO (Kato & Schroeter 1985, Dayton & Tegner 1990), the abundance of sea urchins and diver aptitude). In particular, the two relatively strong El Niños during the 1990s may have affected kelp abundance and consequently gonad quality and catches.

Fishing effort and catch rates In the early years of the ﬁshery there were few restrictions on the number of permits issued and, by 1988, 938 divers were permitted to harvest sea urchins. Many of these divers joined the ﬁshery in anticipation of a moratorium on new permits. This number fell by natural attrition to 537 in 1992. An effort reduction scheme accelerated the process and 421 divers remained in the ﬁshery in 1999. Despite this scheme, considerable latent effort remains in the California ﬁshery; for example, in 1999, 50% of the catch was caught by 22% of divers and 10% of divers ﬁshed for less than 10 days yr−1, and only 12 of the top 100 divers in the ﬁshery worked exclusively in the northern zone. Trends in ﬁshing effort in southern California resemble the serial depletion and decline seen in the Californian abalone ﬁsheries in the mid-1990s (Karpov et al. in press). While the northern Channel Islands have supplied most of the catch over the years, beginning in 1992, catches in the northern islands began to decline as effort and harvests increased from San Nicolas and San Clemente. Recently, ﬁshing effort and catch at San Clemente Island have declined. Whether the harvestable stocks can recover in these heavily ﬁshed areas is unknown, particularly in the absence of effective controls on catch. Catch rates have not been standardised for assessment purposes so trends in raw catch rate must be interpreted with caution. The data are also weakened by ambiguity in effort reporting. Diver-hours, diver-days, vessel-days and receipts (a proxy for diver-days since individual landing requirements were mandated in 1992) have all been used. Catch rates in the southern regions have changed little in the 10 years since the ﬁshery peaked, despite large declines in total catch (see Fig. 16). In the northern region, catch rates fell sharply following the years of greatest landings but have subsequently stabilised and even increased in 1999 (see Fig. 16).

Management Although responsibility for managing the sea urchin ﬁshery rests with the California legislature, this was effectively ceded to the Fish and Game Commission in 1973 (CDFG 1989 as 368

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cited in Kalvass & Hendrix 1997). In the early years of the ﬁshery, management focused on reducing sea urchin densities as a way to increase kelp abundance and gonad yield (Kato & Schroeter 1985). It became apparent that this perspective was too narrow and, in 1987, the legislature established the Director’s Sea Urchin Advisory Committee consisting of representatives from the ﬁshing industry, California Department of Fish and Game and California Sea Grant. An important role of the committee has been to act as a forum for consensusbased management. This approach has been effective – almost all of the management measures introduced originated from this committee. California’s sea urchin ﬁshery has been subject to relatively passive regulation and operates without a ﬁshery management plan. There was no regulation of the ﬁshery before 1985 and few restrictions on catch or effort until the late 1980s (Kalvass & Hendrix 1997). Although a permit entitles a diver to harvest sea urchins in both the northern and southern regions, small differences in regulations have evolved in recognition of differences in the oceanography, ecology and ﬁsheries of the two regions. The principal management interventions have been: (a) a moratorium on the issue of new permits since 1987, (b) the introduction of a MLS in 1988 (increased in the northern region in 1990 and in 1992 in the south), (c) restriction in 1990 of ﬁshing in the northern zone to 233 days yr−1, (d) restriction in 1992 of ﬁshing in the southern zone to 240 days yr−1, and (e) introduction of effort reduction in 1990; this currently requires 10 permits to be retired for each new entrant. All these regulations remain in effect. While the limited entry programme has created a slow but steady decline in the number of permits, it has not signiﬁcantly reduced potential effort in the ﬁshery (Kalvass & Hendrix 1997). Declines in catch and catch rate in the northern region have been the subject of considerable concern (e.g. Kalvass 1992, Botsford et al. 1993, Quinn et al. 1993, Rogers-Bennett et al. 1995). Fishery-dependent modelling of the sea urchin ﬁshery during the period of rapid decline (1985 to 1994) estimated that the 50 800 t of red sea urchins harvested from 1988 through 1994 represented about 67% of the ﬁshable stock available at the start of 1988. Effort declined during this period as the 126 divers who had worked exclusively in northern California during 1991 had dwindled to 69 by 1995. Concomitantly, annual catch per permit declined by 57% from 1990 to 1995. To test whether this decline was due to ﬁshing down, Botsford and colleagues (Botsford et al. 1998) estimated the decline in abundance expected from ﬁshing down using the changing shape of the size distribution, and compared it with the decline in abundance, as estimated from catch rates. This analysis indicated that three of the four major ports in northern California were over-ﬁshed, in the sense that CPUE declined more rapidly than would be expected on the basis of the changing shape of the size distribution. However, only one of the differences was statistically signiﬁcant. The authors concluded that: (a) most of the decline was due to the ﬁshing down effect, but (b) the ﬁshery seemed to be near the point of affecting recruitment. In southern California, the red sea urchin resource has remained productive but harvestable stocks (sea urchins larger than the MLS and containing marketable gonads) have apparently been in decline since about 1990. Although ﬁshing has signiﬁcantly reduced density in many areas and catch rates have declined, strong but localised recruitment has thus far replenished populations. Consistent recruitment has been noted on artiﬁcial settlement substrata and along subtidal transects over the last decade at monitoring stations along the southern California mainland coast and the northern Channel Islands (Ebert et al. 1994, S. Schroeter, unpubl. data). This may be the result of ocean circulation patterns in the Southern California Bight that increase the chances of larval retention and subsequent settlement. 369

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As the accumulated stocks of large sea urchins are removed, the ﬁshery is increasingly dependent on recruitment to sustain harvests. In northern California, the mode of the size distributions declined from 120 mm in 1990 to 95 mm in 1999 (P. Kalvass, unpubl. data; see also Kalvass & Hendrix 1997). Since 1990, the proportion of sea urchins in the catch within 10 mm of the MLS has risen from 38% to 47%. In southern California, mean test diameter declined by 9%, from 102 mm in 1998 to 93 mm in 1999. Almost 65% of the red sea urchins harvested were less than 95 mm in 1999, compared with 50% in 1990. The effect of these changes is 2-fold: more animals are removed from the stock per ton of roe recovered (Kalvass & Hendrix 1997) and greater reliance is placed on recruitment to sustain the ﬁshery. Currently, there are indications that a pulse of recruitment in the northern region observed in 1992–93 is reaching the ﬁshery (P. Kalvass, pers. obs.). The abundance of sea urchins has been estimated in California for many years and for many reasons. Surveys have been done by university-based researchers, the National Park Service and the Department of Fish and Game (e.g. Deacon 1973, Tegner & Dayton 1981, Rowley 1989, Kalvass et al. 1991, Tegner & Dayton 1991, Ebert & Russell 1992, Kalvass & Taniguchi 1993, Pentony 1996, Kalvass & Hendrix 1997, Morgan et al. 2000a). These studies have provided a wealth of information but had different objectives, little methodological consistency, and geographic coverage that was patchy and inconsistent in time. Surveys in the northern region indicate that densities of harvestable stocks have declined signiﬁcantly since 1988 (Kalvass & Hendrix 1997, Botsford et al. 1998, Morgan et al. 2000a, Karpov et al. in press). There have been several attempts to provide quantitative assessment advice for the ﬁshery. In 1997, Kalvass & Hendrix used a Leslie depletion model to estimate pre-ﬁshing biomass. This approach assumes that estimates of catch rate regressed on cumulative catch are from a population that is closed except for ﬁshing removals (as the study was done over several years, recruitment is likely to have caused a negative bias in the biomass estimate). Morgan et al. (2000b) used the model-based method of Smith et al. (1998) to estimate natural mortality, ﬁshing mortality and growth rates at 14 sites along the northern California coast. From these they assessed the dependence of yield-per-recruit (YPR) and egg-per-recruit (EPR) on size limits and ﬁshing mortality rates (Morgan et al. 1999, 2000b). The former indicated the current size limit and effort produce a value of YPR near the maximum, and the latter indicates EPR was <20% of the unﬁshed value. Because the latter was in the range that can lead to recruitment overﬁshing (e.g. Mace & Sissenwine 1993, Mace 1994), they assessed the effects of implementing marine reserves (Botsford et al. 1993, Quinn et al. 1993). Theoretical results on the sustainability of marine reserves indicate the two main uncertainties in their design are the poorly understood larval dispersal patterns and the unknown minimum tolerable lifetime reproduction (Botsford et al. 2001). Botsford et al. (1999) used decision analysis (Hilborn & Walters 1992) to account for the latter uncertainty, which indicated that placing 15% of the coastline in reserve led to the greatest long-term yields. Accounting for the possible patterns of larval dispersal is more difﬁcult, but an initial decision analysis approach is described in Morgan et al. (1999). Prior to the passage of the California Marine Life Management Act in 1998, legislation required that consideration be given to “regulating the catch within the limits of maximum sustainable yields (MSY)” (DFG Code, Sec. 1700). Nevertheless, MSY has not been given an operational deﬁnition and there remains no formal assessment to guide management intervention. As a result, management has largely been ad hoc and consensus-driven (Kalvass 1992, Kalvass & Hendrix 1997). The present management regime is based on size limits and 370

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restricting ﬁshing effort but is unlikely to prevent any increase in catch and ﬁshing effort that may be caused by perceptions of improved proﬁtability. The Marine Life Management Act has the potential to change the direction of sea urchin ﬁshery management in California because it requires the development of peer reviewed management plans that incorporate the best available science in management measures that ensure the continued viability of the sea urchin ﬁshery and its ecosystem. Another recent law, the Marine Life Protection Act, mandates the implementation of marine reserves, which is now in progress. It remains to be seen whether the implementation of these laws will deliver improved management to California’s ﬁshery, particularly in the face of external threats such as an expanding sea otter distribution and large-scale oceanographic events that may affect the dynamics of sea urchin populations (see Tegner 2001 for review).

Baja California (Mexico) The ﬁshery The Baja California ﬁshery is based on two species: the red sea urchin Strongylocentrotus franciscanus and the purple sea urchin S. purpuratus. Both are at the southern limits of their distributions. These species co-occur with other sea urchins along the Baja coast, (e.g. Centrostephanus coronatus and Lytechinus anamesus), but are the only species of economic signiﬁcance. The red sea urchin is one of the largest species of sea urchins in the world, growing to about 200 mm and weighing more than 1 kg. The purple sea urchin is small, growing to about 70 mm. The ﬁshery is limited to the northern third of the peninsula, from the border with the United States to El Rosario Bay. This 450-km stretch of coastline is divided into four management areas. Of these, the most southern area was the most productive but catches in the neighbouring area were more predictable and, in recent years, that area has assumed greater importance. The ﬁshery for Strongylocentrotus franciscanus began in 1972 and has always supplied the Japanese market. The ﬁshery grew quickly and is characterised by three peaks in catch, in 1979, 1986, and 1990. S. purpuratus has been harvested since 1993 but catches have remained relatively small. Densities of purple sea urchins have risen in the past 10 yr, perhaps as a consequence of removing the competitively superior red sea urchin (Schroeter 1978) but inconsistent roe quality has retarded development of the ﬁshery. Sea urchins are collected by divers using surface-supplied air from depths to 30 m. The divers use short-handled rakes to dislodge sea urchins from the reef and, as they do in other ﬁsheries where they are paid on roe recovered, divers open sea urchins to test roe quality as they work. There is no recreational ﬁshery and the illegal harvest is believed to be small. The ﬁshery expanded rapidly and by 1979 landings had reached 5800 t yr−1 (Fig. 17). Only 1590 t was landed the following year but this reduction was caused by the combination of El Niño, marketing problems and associated reductions in ﬁshing effort (J. Palliero, pers. obs.). The ﬁshery again expanded quickly, reaching 8493 t in 1981. Regulations introduced in 1987 brought greater stability in the ﬁshery; catches fell through the 1990s as increasingly restrictive management measures were introduced and were at their lowest levels in 1998– 99. Landings in the last ﬁshing year, 1999–2000, rose sharply again, to 2153 t (Fig. 17). The number of divers has remained relatively constant since the introduction of management (300 in 1987 and 291 in 1999–2000). About 40% of divers in 1999–2000 worked in 371

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the most southern management area in the ﬁshery, the rest divided roughly equally among the other three areas. Catch rates for S. franciscanus have declined signiﬁcantly from a mean rate of 309 kg day−1 in 1989 to 104 kg day−1 in 1999–2000 (Fig. 17). The declines were greatest in Area 4 where catch rates fell from 390 kg day−1 to 138 kg day−1 in 1995 (Cota et al. 1996).

Management The red sea urchin ﬁshery was unregulated until 1987 when it was transformed from an open-access competitive model to one in which permit holders were given exclusive access to areas or “territories”. Before 1987, divers could ﬁsh anywhere and many areas were severely depleted. In 1994, the Federal Government gave permit holders (these may be individuals, co-operatives or other social institutions) two-year leases (and on application 20-yr leases) for exclusive access to areas. Permits are tradable and these changes provide investment security and promote greater commitment to long-term sustainable use. In 2000, there were 48 permit holders, each with an exclusive right to an area of sea bed; a total of 291 divers were employed to harvest sea urchins within these 48 areas. Other management measures introduced in 1987 included a MLS of 80 mm (red sea urchins on the Baja California coast are sexually mature at 41–50 mm (Palliero et al. 1992) ), a closed season between April and June (later extended to March–June), and a TAC for the whole ﬁshery. In addition, catch and effort reporting was required. In 1996, industry and government placed a moratorium on new permits and limited divers to 150 kg day−1 and ﬁshing to ﬁve days per week. The TAC is used as a precautionary instrument only; each year it is set by government based on an analysis of survey and catch and effort information (e.g. Cota et al. 1996). If the quota is caught within the ﬁshing year (this has happened twice) then further quota may be 372

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approved. The total catch depends to a greater degree on the roe recovery rates, beach price and abundance. For example, in the 1998–99 ﬁshing year, Macrocystis forests were severely depleted as a result of an El Niño event (CALCOFI 1999) and roe were poor. This, along with a depressed market in Japan, meant that the TAC was not caught. Assessments of the red sea urchin ﬁshery are based on annual analyses of ﬁshery-derived information such as catch rate and mean size in the landed catch, and ﬁshery-independent surveys (e.g. Palliero et al. 1992, Cota et al. 1996). About half the standing stock of legalsized red sea urchins is caught each year (Cota et al. 1996). Since assessments began there have been declines in catch rates and the mean size of sea urchins (from 93 mm in 1991 to 78 mm in 1996 (Cota et al. 1996) ). Video surveys of reefs also indicate declines in densities of red sea urchins, particularly within Macrocystis forests. Densities of juvenile red sea urchins are greatest on the margins of the forests. There is evidence that purple sea urchins have increased in density and have replaced red sea urchins in deeper water (Cota et al. 1996).

New Brunswick (Canada) The green sea urchin, S. droebachiensis, forms the basis of the New Brunswick ﬁshery. Sea urchins are most abundant in waters less than 10 m deep (Robinson & MacIntyre 1993, 1995), and are harvested by dragging or diver-based techniques (either hand gathering or suction harvesting). The ﬁshery initially developed to supplement ﬁsheries in the United States; small landings (<50 t) were reported until the early 1990s when catches increased dramatically (DFO 2000a). Landings peaked in 1996 at 1900 t and have fallen slightly since then, partly because of the introduction of quotas (Fig. 18). The ﬁshery is divided into three management zones, two of which (Grand Manan Island and the mainland and coastal islands) account for almost all the catch. Fishers are permitted to ﬁsh in only one of these two zones, plus the third zone. A MLS of 50 mm applies to the 2000 1800

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whole ﬁshery. Fishing is restricted to a 6-month (Grand Manan) or 8-month (mainland and coastal islands) season and harvesting is limited to daylight hours. Since 1995, dragging has been the only method permitted in the Grand Manan ﬁshery; both dragging and diving are allowed on the mainland and coastal islands. In addition to these input controls a TAC provides a theoretical limit to catch in both management areas. These TACs were implemented in 1996 and are derived from biomass estimates from a dive survey in 1992–94 and ageing studies (Robinson & MacIntyre 1997, DFO 2000b). For Grand Manan Island, the 900 t TAC was 6.8% of the estimated exploitable biomass of 13 245 t. The TAC for the mainland and coastal islands was set at 979 t, 3.3% of an estimated biomass of 29 879 t. The differences in exploitation rate are based on perceived differences in productivity between areas; juveniles were present in greater numbers in surveys from Grand Manan (DFO 2000a). The TAC is divided among permit holders as individual transferable quotas in Grand Manan but not the mainland management area. Neither TAC has been caught since they were introduced. Stock status is determined annually from catch rates from mandatory logbooks (DFO 2000b). Catch rates in the drag-only ﬁshery at Grand Manan Island have declined from a peak of nearly 1600 kg day−1 in 1994 to 824 kg day−1 in 1999 despite a shift in ﬁshing grounds towards the south of the island in an attempt to maintain catch rates. The mean size of sea urchins in the landed catch declined over this period. There are no consistent trends in catch rate or the size of sea urchins in the catch from either dragging or diving in the mainland and coastal islands (DFO 2000a). Current assessments are considered inadequate (DFO 2000a) and there has been no management intervention in either ﬁshery since the imposition of TACs in 1996.

Russia and the former USSR Sea urchin ﬁsheries in Russia are now found only in the far east, although harvesting has been reported from the Barents Sea (Keesing & Hall 1998). Sea urchin ﬁsheries are centred on the Japan Sea, the Kamchatka Peninsula and the Kuril Islands (Bazhin 1998, Keesing & Hall 1998). Russian landings are dominated by the ﬁshery for Strongylocentrotus intermedius in the Japan Sea and Sakhalin Island. Little is known of this ﬁshery although small amounts of S. nudus were landed in 1998 (V. Levin, as cited in Keesing & Hall 1998). Further north, on the Kamchatka Peninsula, from Cape Lopatka to the Commander Islands, only S. polyacanthus is harvested. Remoteness and heavy ice for much of the year has impeded development of these ﬁsheries. A ﬁshery in the Kuril Islands targets S. intermedius and in 2000, Japan imported 3300 t of sea urchins from the Kiril Islands, most of which was processed in Hokkaido (Y. Agatsuma, pers. obs.). Although S. droebachiensis and S. pallidus may be abundant and are considered to be potential ﬁsheries (Bazhin 1998), few have been harvested in recent years. Most sea urchins landed in Russia are shipped live to processing plants in Hokkaido. There are no recreational or customary ﬁsheries for sea urchins and illegal harvesting is believed to be small. FAO catch statistics for USSR/Russia for all echinoderms appear to be incomplete and are considered unreliable by some authors (e.g. Reid & Ovichinnikov 1995, as cited in Keesing & Hall 1998). Catches from the Soviet Union before 1986 are small and sporadic (Fig. 19) but jumped to 6328 t in that year, the highest reported catch. Catches from Russia in 1992 fell to less than half that before the break-up of the Soviet Union in 1992. 374

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Each year since 1992, catches of S. polyacanthus have been less than 90 t (<6% of the total reported catch in Russia) and less than a quarter of the allocated TAC of between 280 t and 540 t. Fishing has caused measurable changes in the population structure at many locations, particularly in the southeast (Bazhin 1998), but there are no consistent indications that ﬁshing has signiﬁcantly diminished stocks. Densities have increased on some ﬁshing grounds (A. Bazhin unpubl. data) and there is evidence that large animals lost to the ﬁshery are replaced by smaller sea urchins migrating inshore from deeper water, often from areas of barrens habitat (Bazhin 1998, A. Bazhin, unpubl. data). All Russian sea urchin ﬁsheries are managed by a mixture of input and output controls (see Ivanov 1998 for review). The TAC for regional ﬁsheries is set by the Ministry of Fisheries and other central agencies in Moscow, following advice from the regional ﬁsheries laboratories. For sea urchin ﬁsheries on the Kamchatka Peninsula this advice is based on ﬁshery-independent surveys and information derived from the ﬁshery. A portion of the TAC is caught under supervision from scientiﬁc observers and is allocated to the regional ﬁsheries research institutions as a quota. This quota is purchased by ﬁshers at a discount and distribution and size structure of the catch is well-documented. Companies ﬁshing the “scientiﬁc quota” are permitted to ﬁsh during the closed season. The remainder of the TAC is caught by ﬁshers who are required to provide daily reports of catch and location. Initially this was intended to provide a mechanism for within-season adjustment of the TAC, but in practice the centralised control of quota-setting allows little ﬂexibility. A MLS of 50 mm is in place and a closure between 15 September and the end of December ostensibly protects spawning sea urchins.

Alaska (USA) The sea urchin ﬁshery in Alaska targets primarily red sea urchins (S. franciscanus) in the state’s southeastern panhandle and green sea urchins (S. droebachiensis) in the northern 375

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Gulf of Alaska. Commercial harvests began in 1980, but early catches were sporadic and small (peaking at 343 t in 1987, Fig. 20) and the ﬁshery was limited by marketing problems. Prior to 1990, in the early development of the ﬁshery, harvesting was restricted to permitted divers but there were few other restrictions. A second attempt to develop the ﬁshery began in the Sitka area in 1990. Total catch was limited to 2% of the biomass estimated from stock assessment surveys. Catches remained small (<200 t, Fig. 20) and this ﬁshery ended in 1993 after sea otters expanded their range into the ﬁshing area. The sea otters removed an estimated 64% of the sea urchin population in one winter, with depredations continuing at unknown levels in subsequent years (see below). The third and current attempt to develop the ﬁshery began in 1994 in southeast Alaska near Ketchikan in areas free of sea otters. Catches expanded rapidly and, in 1997, 2921 t was landed (Fig. 20). In 1999, approximately 1420 t was landed. Commercial harvest levels are now based on biomass estimates derived from population surveys. In 1997 a management plan and regulations established an open competitive commercial ﬁshery in nearly all of the viable commercial ﬁshing grounds of southern southeast Alaska. The plan is based on an agreed exploitation rate calculated from a surplus production approach similar to that used for the sea cucumber ﬁshery in southeast Alaska (Woodby et al. 1993, see also British Columbia, p. 363). Total allowable catches for each of the 24 areas in the ﬁshery are calculated as a small fraction of biomass (Gulland 1971, Caddy 1986): TAC = XMB, where: X (= 0.4) is a scaling factor, M is the estimated instantaneous natural mortality rate (estimated to be 0.16 near Sitka, Woodby, 1991), and B is the lower bound of the 90% conﬁdence interval of triennial biomass estimates from ﬁshery independent surveys (Woodby 1998). Densities are usually estimated as the number of sea urchins per metre of shoreline, and biomass is calculated from mean weights and the shoreline length of surveyed habitat. Density estimates based on benthic area are used only for a few offshore reefs. Surplus production models are commonly used in data-limited situations (Caddy 1986), but are simplistic and risky if not applied cautiously (e.g. Garcia et al. 1989). The main safeguards in their application to the Alaskan ﬁshery are conservative estimates of biomass used. Continued collection of survey information and completion of research on growth and 376

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mortality may soon allow better estimates of sustainable yield. In two experimental harvest areas, the exploitation rate has been allowed to reach 20% per year. The purpose of this experiment was to harvest at a rate high enough to elicit a detectable population response, such as increased recruitment or changes in growth rates. Unfortunately demand for sea urchins has been too weak to achieve those high rates and the exploitation rate has ranged from less than 1% to 17% in each of the past two years in the two areas. The ﬁshery opens on October 1 each year and each area is closed when the TAC is taken. There are no rotational closures. The TAC has not been caught in any of the four years since the current management plan was adopted so some areas have remained open all year. There are currently 95 transferable permits in the ﬁshery. There are no size limits in the ﬁshery and harvest methods are limited to picking sea urchins by hand using a rake or an abalone iron. One ﬁshing area and a portion of another have been set aside as control areas which are surveyed every year to follow trends in unﬁshed populations. Fishing is prohibited within 3 nautical miles of four islands with Steller sea lion (Eumetopias jubatus) colonies.

South Korea Sea urchin harvesting has a long history in South Korea but accounts for only a small proportion of the total seafood harvest (Korean Fishery Association 2000). About 40% of the total sea urchin harvest is exported to Japan. Of the four species of sea urchin commercially harvested, Anthocidaris crassispina, Hemicentrotus pulcherrimus, Pseudocentrotus depressus and Strongylocentrotus intermedius, the last is the most valuable. Its current price is about US$45 kg−1 of roe. The prices of Hemicentrotus pulcherrimus and Anthocidaris crassispina roe are approximately US$35 kg−1 and US$25 kg−1, respectively. Fishing seasons differ among species and areas but harvesting for each is concentrated in the month prior to spawning. During the spawning season, the roes have a bitter taste and become soft, making them less valuable. In general, A. crassispina and Hemicentrotus pulcherrimus spawn at Jeju Island in July, during August on the south coast and during September on the east coast (Yoo et al. 1982). Anthocidaris crassispina is widely distributed on all coasts of South Korea but the ﬁshery is concentrated on the rocky reefs of the east coast, facing the Sea of Japan (Hur et al. 1985, Yoo 2000). Similarly, ﬁsheries for Hemicentrotus pulcherrimus and Strongylocentrotus intermedius are concentrated on the east coast, in Kyungnam province and in Kyongbook province (Hur et al. 1985). S. intermedius is found in deeper waters in the north of the province where the water temperature at 10 m falls below 20°C. All species except S. intermedius are harvested from the subtropical waters of Jeju Island, to the south of the Korean peninsula; small catches of Pseudocentrotus depressus are taken from Jeju Island but these are not differentiated from Anthocidaris crassispina. Sea urchins are relatively rare on the muddy intertidal and shallow subtidal shores of the west coast; small numbers of A. crassispina and Hemicentrotus pulcherrimus are found on isolated rocky islands. In 1999 nearly 90% of sea urchins harvested in South Korea were caught by women (known as “Hae-nyeo” or “women of the sea”) who breath-hold dive. In addition to sea urchins these women harvest abalone, topshells, sea cucumbers and other benthic invertebrates. Skilled divers catch mainly Anthocidaris crassispina at depths near 10 m. Sea urchins are also caught by divers working from boats using surface-supplied air. Sea urchins are only a minor component of the catch of these latter divers, who target large clams such as Mya 377

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arenaria oonogai and Tresus keenae, cockles (Anadara broughtonii) and sea pens (Atrina pectinata) at depths below 10 m. Historically, catches using this method were more important but have declined from nearly 50% of the commercial catch in the 1970s (Ministry of Maritime Affairs and Fisheries 1999). Large-scale commercial harvests began in the 1960s and peaked at 7751 t in 1986 (Fig. 21). After 1986 catches sharply declined, were relatively stable through most of the 1990s before again falling. In 1999 only 1182 t of sea urchins were landed (Fig. 21). In 1999, the production of sea urchins from the east coast (Kangwon and Kyoungbook provinces) accounted for 62% of total production, and that from the south coast and Jeju Island 28% and 10%, respectively. Production from the east coast and Jeju Island peaked in 1986 and in 1981 on the south coast (Fig. 22). There are no production statistics by species. Approximate 378

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species proportions may be estimated based on interviews with divers and season of harvest. In 1999, Anthocidaris crassispina and Hemicentrotus pulcherrimus each accounted for 40– 45% of the total harvest (S. Hur, unpubl. data). Of the remainder, Pseudocentrotus depressus and Hemicentrotus pulcherrimus accounted for nearly 10% and Strongylocentrotus intermedius the remainder (about 30 t in 1999). The dive ﬁsheries, in waters less than 10 m deep, are owned and managed by local ﬁshing villages. These ﬁsheries are required to report statistics to the government (Ministry of Maritime Affairs and Fisheries 1996). Fishing is restricted to women living in each village. In general, there are no MLS or other limits on catch but the village decides how many divers can work and when. In 1996, 10 414 divers harvested sea urchins; this declined to 8976 in 1999. The mean catch rate of divers has fallen, from 308 kg yr−1 in 1993 to 115 kg in 1999. Fishing in water deeper than 10 m is managed by the Provincial Government and is restricted to licensed vessels. The number of vessels is limited by a moratorium and has declined from a peak of 681 in 1970 to 236 in 1997. Catch rates in this ﬁshery peaked at a mean of 9.0 t vessel-yr−1 in 1986, but have declined each year since and, in 1999 a mean of only 0.6 t was caught vessel-yr−1 (Fig. 21). Most species are widely distributed to depths beyond the breath-hold capacity of divers. Consequently, the decline of sea urchin stock may not be due to over-ﬁshing, at least in this sector. The decline may have been promoted by environmental factors, particularly pollution and macroalgae resources (see p. 401) but this conclusion cannot be considered robust in the absence of stock assessments.

Nova Scotia (Canada) The green sea urchin, S. droebachiensis, is common on shallow rocky reefs around Nova Scotia (see Scheibling & Hatcher 2001 for a recent review) but is harvested mostly from counties on the Atlantic coast. A largely unexploited resource is thought to exist around Cape Breton Island. The ﬁshery is dive-only and operates to depths of 15 m. It began in 1989 although the catch remained at less than 100 t per annum until the 1993–94 ﬁshing season when there was an explosive increase in ﬁshing effort, largely brought about by rises in the beach price (Fig. 23a). Almost all the catch is exported to Japan in some form. Most of the best quality sea urchins are shipped directly as whole animals, about 20% are processed in Nova Scotia and the remainder in Maine, before export to Japan. Most landings have come from Guysborough and Shelburne counties, but in 1998–99 and 1999–2000, Digby county accounted for 19% and 43% of the total catch, respectively. There is no recreational ﬁshery for sea urchins in Nova Scotia and the illegal harvest is small. The ﬁshery is managed by the Department of Fisheries and Oceans (DFO). Until recently, DFO limited the number of licenses per section of coastline, usually a county, based on an assessment of the number of licenses that might be supported (DFO 2000a). Fishers competed for catch but there is no limit on total catch within each area. New entrants were required to land at least 4 t per year to retain their license. There are no ﬁshing seasons or Marine Reserves in Nova Scotia but a 50-mm MLS is observed. Thirty per cent of the licenses have been given to First Nation ﬁshers as communal licenses. They have not been required to meet participation requirements, but otherwise abide by the same rules as commercial licensees under the Fisheries Act. In a 1992 decision of the Supreme Court of Canada, First Nation people were granted the right to take a small amount of any commercial species for food and ceremonial purposes, but they have rarely 379

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exercised that right. In a 1999 decision by the Supreme Court, First Nation ﬁshers were furthermore allowed to take and sell enough ﬁsh to make a “moderate livelihood” under regulation by the management agency. Arrangements to comply with the second ruling are at present being negotiated. 380

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Management is evolving away from this competitive form of ﬁshing to a unique system of area-based management in which individual licensees (or permit holders) have exclusive access to stretches of coastline (Miller & Nolan 2000). So far as we are aware, in all other area-based systems of management, “ownership” is vested in groups or communities rather than individuals (but see pp. 393–405). Fishers may apply for a “restricted zone” after meeting speciﬁed guidelines: demonstrating a history of ﬁshing sea urchins, providing a map of kelp and sea urchin distribution in the proposed zone (see below), promising to increase the habitat carrying capacity for marketable sea urchins, and promising to detail catches and ﬁshing locations. The area requested is then surveyed by DFO and either the zone is granted or new borders are negotiated. After a four-year trial period the zone is surveyed again by DFO which then decides whether the zone has been fully used. If a signiﬁcant portion of the beds has not been ﬁshed then a reduced zone size is negotiated. This form of management was initiated in 1995 and continues to develop, despite ﬁsher resistance to changes in the size of zones. The MLS of 50 mm is still in force in the restricted zones. Exclusive access has many advantages, including the ability to harvest sea urchins at times and places that maximise roe recovery without competition. Furthermore, the beneﬁts of enhancement, such as moving sea urchins to areas with more algae, accrue to the individual who invests time and money in the work. The core of this management is the requirement for ﬁshers to maintain a dynamic balance between sea urchin densities and kelp (Miller & Nolan 2000). On the Atlantic coast of Nova Scotia sea urchins are most abundant in dense aggregations or “feeding fronts” that occur as bands at between 1 m and 15 m depth. Above this band there are dense kelp forests; below it sea urchins are less dense but sufﬁciently abundant to maintain the barrens habitat (Chapman 1981; see Scheibling & Hatcher 2001 for the most recent review of the extensive ecological literature). The location of the feeding front along this depth gradient is determined by exposure to wave action, the time of year, and sea urchin density. As the sea urchins consume the algae, the aggregations move shoreward at a rate of 1– 4 m per month (Breen & Mann 1976, R. E. Scheibling, unpubl. data as cited in Scheibling & Hennigar 1997). Fishers target the feeding fronts because roe recoveries are greatest there and ﬁsheries managers use this relatively simple one-dimensional resource to simplify assessments. Within the restricted zones, assessments are based on the depth at which these bands are found as an index of exploitation and the length of the band along the shore is used as an index of the size of the resource (DFO 2000b, Miller & Nolan 2000). A series of species invasions in recent years has disrupted the dynamics of interactions between kelp and sea urchins (see Scheibling 2000, Scheibling & Hatcher 2001 for review), and these changed dynamics pose a major threat to the ﬁshery. The ﬁrst and best documented invasion was that of a parasitic amoeba Paramoeba invadens (see Scheibling & Hatcher 2001 for review). In the early 1980s more than 270 000 t of sea urchins were killed by the parasite (Miller 1985, Moore et al. 1986). Since its re-appearance in 1994, the parasite is thought to be responsible for the death of more than 100 000 t of sea urchins (R. J. Miller, unpubl. data). In Halifax, Lunenburg, Queens and Shelburne counties, mass mortalities from disease have reduced sea urchin densities below economically viable levels. In Guysborough county, at least 60% of the stock was lost to disease in 1999–2000 (Miller & Nolan 2000). Most other counties have not experienced signiﬁcant disease related mortality, and there remains a substantial sea urchin resource, much of which is currently unexploited. Sea urchins below the seasonal thermocline are believed to escape the disease and these sea urchins re-establish populations in shallow water, a process taking up to a decade (Scheibling 381

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2000). There is a need to develop a predictive model for the spread and effects of disease on the sea urchin and other nearshore ﬁsheries. A bryozoan, Membranipora membranacea, has colonised kelp forests in some areas (Scheibling et al. 1999). The appearance of the bryozoan, ﬁrst observed in 1992, may accelerate the demise of kelp forests by reducing biomass (Dixon et al. 1981) and increasing the rate at which the feeding fronts move inshore (Scheibling et al. 1999). Coincident with the appearance of the bryozoan, the green alga Codium fragile has also appeared on the Nova Scotia coast (Chapman 1999). This species recruits to areas of what were barrens habitat following mortality of sea urchins and into kelp forests whose canopy has been disrupted by the bryozoan. C. fragile can out-compete kelp and thus has the potential to change the cyclic nature of habitat structure in Nova Scotia (Scheibling 2000). Although sea urchins will consume C. fragile, their roe is of a poorer quality (Prince & LeBlanc 1992, Scheibling 2000). The Nova Scotian sea urchin ﬁshery has one of the most innovative management strategies anywhere. The exclusive access in restricted zones offers the potential to maximise the economic return from the resource while satisfying the management agency’s responsibilities for sustainable harvest. Refuges are provided by barrens habitat too deep for divers to harvest and in areas where sea urchins are too few or have roe recovery too small to harvest economically. Spawning is protected by the MLS: green sea urchins become reproductive at about 25 mm (Miller & Mann 1973, Wahle & Peckham 1999), well below the MLS of 50 mm. Perversely, however, the re-emergence of disease (Scheibling & Hennigar 1997) and the complicating effects of other invading species makes long-term sustainable harvest a difﬁcult management objective. The effects of these invaders and the timescale over which they operate makes predictions highly uncertain. Under these circumstances, the explicitly experimental nature of the restricted zones offer possibly the best chance of maximising yield and increasing understanding of these complex dynamics.

Philippines Sea urchins are a common part of the diet of many coastal communities and are harvested by shore-collecting and diving on reefs where seagrass and algae (e.g. Sargassum) are abundant. The most commercially important sea urchin species is Tripneustes gratilla although other species, such as Diadema setosum are also harvested for local consumption (JuinioMeñez et al. 1995, 1998, Talaue-McManus & Kesner 1995). One of the most important regional ﬁsheries in the Philippines is in northwestern Luzon. There is a large domestic market centred on Manila and most of the exported product goes to Japan as fresh whole sea urchins or processed roe products (unpubl. data from the Bureau of Agricultural Statistics). As with other invertebrate stocks in the Philippines (Ross 1984, Juinio et al. 1989), few reliable statistics are available on sea urchin ﬁsheries. FAO catch data are confounded by the large ﬁshery for holothurians (Trinidad-Roa 1988, Conand & Byrne 1993). FAO reports catches of echinoderms of between 1487 t and 4071 t since 1985 but most of this appears to be holothurians (Conand & Byrne 1993). Nevertheless, catches of sea urchins in the Philippines appear to be substantial; minimum estimates of catches since 1991 may be reconstructed from recorded exports of sea urchin products using unpublished data kept by the Bureau of Agricultural Statistics. Assuming a roe recovery rate of 15.8% (M. A. JuinioMeñez, unpubl. data), a time series of exports from the Philippines since 1991 in whole 382

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animal wet weight may be derived (Fig. 23b). Exports fell sharply between 1994 and 1996 but recovered in the late 1990s before declining again in 1999 to be at their lowest level since 1991 (Fig. 23b). The pattern of development and collapse in the commercial ﬁshery for Tripneustes gratilla in Bolinao is typical of other sea urchin ﬁsheries in the Philippines (Talaue-McManus & Kesner 1995). In Bolinao harvesting for export began in the 1970s and developed through the 1980s as an unregulated open access ﬁshery. In 1988, a seasonal closure was implemented but this was enforced for only two years and did little to slow the decline in catch (Juinio-Meñez et al. 1998) and the ﬁshery collapsed in 1992. Ironically, the Municipal Government prohibited commercial harvesting of sea urchins in Bolinao in 1993, after the ﬁshery collapsed. This prohibition remains in force but commercial harvesting of sea urchins resumed in 1998 after good recruitment occurred for the ﬁrst time since 1992. The total landed catch in 1999 was approximately half of that in 1992 (A. Juinio-Meñez, unpubl. data). Catch rates in 1999 ranged between 0.7 and 2.8 kg roe person-day−1 and are comparable with those reported in 1991 and 1992 (Talaue-McManus & Kesner 1995), prior to the collapse of the ﬁshery. In the wake of this collapse, research on the culture and grow-out of sea urchins was undertaken to rehabilitate the ﬁshery. The grow-out was based partially on the experience of ﬁshers nearby who used grow-out cages on a small-scale to produce high quality sea urchins for the local market (Juinio-Meñez et al. 1998). This system is discussed in more detail in the section on enhancement (pp. 393–397).

New Zealand In New Zealand, sea urchins are harvested in most regions by commercial, recreational and Maori customary ﬁshers (McShane 1992). Although as many as 10 species of sea urchin are caught in commercial ﬁsheries, Evechinus chloroticus is the only species targeted, and it accounts for more than 99% of the recorded catch and is the only species considered here. Most E. chloroticus are found in waters less than 10 m deep and are harvested by breathhold diving although about 10% of the total catch in the 1998–99 ﬁshing year was taken by dredge. Almost all of the roe harvested in this ﬁshery is consumed on the domestic market. Customary Maori catches are poorly described but the total non-commercial harvest may be as much as 50% of the commercial catch. Commercial harvesting is concentrated in ﬁve of the ten ﬁsh-stocks. Commercial catch peaked in 1992–93 when 1032 t was landed (Fig. 23c). Commercial harvest has declined each year for the last ﬁve years and a total of 663 t was reported in 1998–99. No catch statistics are available before 1983. Participation in the commercial ﬁshery declined through the 1980s, rose to a peak of 138 ﬁshers in 1991–92, then fell to 71 in 1999. There is considerable latent effort in the major ﬁsheries: in 1998–99 more than 83% of the total catch was landed by 20% of the permit holders. These patterns, along with the overall decline in catch since 1993 is consistent with a “race for property rights” prior to imposition of the permit moratorium and in anticipation of the allocation of commercial individual transferable quota, in 2002. Catch rates from compulsory logbooks form the only time series of relative abundance data. Patterns are erratic in all major ﬁsheries and probably do not reﬂect trends in stock size. Commercial ﬁshing is managed by a range of regulatory measures, including: (a) a moratorium on new permit holders since 1992, (b) limits on ﬁshing methods (currently dredging and breath-hold diving only), (c) competitive TACs and daily catch limits in some 383

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ﬁsheries, and (d) area closures. There are no seasonal closures or size limits in any ﬁshery. The competitive TACs were set “administratively” in 1988 and were not based on an assessment of sustainable yield. Where present, competitive TACs are either not caught or exceeded, both by wide margins. Annual catches have varied erratically in most areas and there have been major declines in catch and effort in several since their peaks in the early 1990s. There is considerable latent effort in all the major ﬁsheries; the catch landed in 1998–99 was taken by a small proportion of permit holders. Although there is a wealth of information on the biology and ecology of this species (see Andrew 1988, Barker 2001 for reviews), there are no estimates of biomass, trends in relative abundance or assessments of sustainable yield for any ﬁshery. An experimental ﬁshery in Dusky Sound was established in the early 1990s to estimate productivity and the effect of ﬁshing on associated biota (McShane 1992). Stock size was to be estimated by depletion methods but this was unsuccessful because only 133 t of the projected 1000 t was caught (McShane et al. 1994). This catch was insufﬁcient to cause a measurable change in estimated biomass. This ﬁshery failed, in part because it could not reliably produce roe of acceptable quality for the Japanese market. No other attempts to develop export markets have succeeded (McShane et al. 1994). Little is known of the ecological effects of E. chloroticus ﬁsheries. The species removes all large brown algae from areas of reef in some parts of the country but not others (Barker 2001). It is likely that large-scale commercial ﬁsheries will have complex effects that need to be managed. Nevertheless, management of New Zealand’s sea urchin ﬁsheries retains a single-species focus.

Spain Information on the ﬁshery in Spain is difﬁcult to obtain: FAO reports catches only since 1996 with a maximum catch of 595 t in 1997. Most of this catch comes from the northern Atlantic regions of Galicia and Asturias, where Paracentrotus lividus is harvested. Sea urchins are collected both in the intertidal zone and from subtidal reefs (Haya 1988) and the majority of the harvest is canned and sold domestically (E. Ballesteros, unpubl. data). Sea urchins are also harvested from the south of Spain, mainly for festivals but little is known of this ﬁshery. On the Mediterranean coast of Spain, sea urchins (P. lividus) are harvested mostly in northern Catalonia (Ballesteros & Garcia 1987, Le Direac’h et al. 1987) and catches have increased in the last two last decades as consumption of sea urchin roe has become more popular. Breathhold diving has become the prescribed method of harvest in Catalonia, where commercial divers must have a licence to harvest shellﬁsh, including sea urchins. The ﬁshery is not capped by a TAC. SCUBA is allowed in other regions in Spain, such as the Balearic Islands. There are no size limits or closed seasons in place in Spanish sea urchin ﬁsheries. The sea urchin resource in Catalonia is considered to be large (Lozano et al. 1995, Turon et al. 1995, Palacín et al. 1998a), largely due to the lack of disease (Boudouresque et al. 1980) and the cascading effects of intense ﬁshing for predators of sea urchins (Sala & Zabala 1996). The estimated abundance of sea urchins >20 mm on reefs in depths <10 m is roughly 280 million individuals for the whole coast of Catalonia (Palacín et al. 1998a). Commercial landings in Catalonia are recorded from 1992, and according to data supplied 384

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by the Fisheries Department they range between 0.5 t yr−1 and 9 t yr−1 (mean = 5 t yr−1 in the last four years). These estimates are considered to signiﬁcantly underestimate the commercial catch because a large proportion of the harvest is sold directly to retailers or otherwise processed and does not pass through the wholesale channels. The true landings in Catalonia, including those from the recreational sector are likely to be between 30 t yr−1 and 40 t yr−1 (E. Ballesteros, unpubl. data) or 1% of the total harvestable population on reefs in <10 m depth (Palacín et al. 1998a). There is growing evidence that densities of sea urchins are increasing in northern Catalonia, and threatening populations of large brown algae (Verlaque 1984, Palacín et al. 1998b).

Washington (USA) The ﬁshery Washington’s commercial ﬁshery for red sea urchins (Strongylocentrotus franciscanus) began in 1971 and quickly developed into a 700 t yr−1 ﬁshery (Fig. 24). The ﬁshery extends from the San Juan Islands to Cape Flattery, at the mouth of the Strait of Juan de Fuca. Catches in the early 1980s slumped because poor roe quality disrupted market conﬁdence but they rebounded to a peak of 4024 t in 1988. Landings ﬂuctuated for the next four years before declining sharply in 1993 to 503 t and have slowly declined since to a low of 217 t in 1999 (Fig. 24). Beginning in 1986, a small ﬁshery developed for green urchins, S. droebachiensis. This ﬁshery also peaked in 1988, when 464 t was landed (Fig. 24). Since then catches have ranged between 442 t in 1992 and 91 t in 1998 with no consistent pattern. Because quotas have remained stable for green sea urchins since 1995, market quality and the timing of the spawning season are the primary inﬂuences on catch. Catch rates of red sea urchins increased through the 1970s as the ﬁshery developed and peaked in 1985 at 2536 kg vessel-day−1 (Fig. 24). Catch rates plummeted the following year

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to only 1412 kg vessel-day−1 and steadily declined through most of the 1990s (Fig. 24). A sharp drop in catch rate in 1995 may be attributed to the entry of large numbers of new divers following the advent of the tribal ﬁsheries (see below). In 1999 catch rates were slightly over half those in 1986. Catch rates of green sea urchins have also declined over the life of the ﬁshery but the changes are both less extreme and less consistent. Catch rates for red sea urchins have risen in the last few years and preliminary estimates for the year 2000 suggest they may be as high as 1085 kg vessel-day−1 (A. Bradbury, unpubl. data). Catch rates for green sea urchins have remained relatively consistent since 1996 (Fig. 24).

Management The red sea urchin ﬁshery was regulated only by licensing and catch reporting requirements before 1977, when a range of measures was introduced. Of these, the most signiﬁcant was a rotational ﬁshing scheme in which harvesting was limited to two of ﬁve zones that were rotated every three years (in the third year of rotation only one of the ﬁve zones was ﬁshed). In 1977 ﬁshing was restricted to winter and size limits were introduced to protect the smallest 20% and largest 20% of the population. At the San Juan Islands, sea urchins between 102 mm and 140 mm were vulnerable to the ﬁshery and those in the range 83– 114 mm could be harvested in the remaining three zones in the Strait of Juan de Fuca. The introduction of a MxLS was based on the observation that juvenile red urchins were abundant under the spine canopy of adults (Bernard & Miller 1973, Tegner & Dayton 1977, and see Cameron & Schroeter 1980, Breen et al. 1985). The MxLS was not well-observed by the ﬂeet and over-legal-sized sea urchins constituted a signiﬁcant proportion of the catch (Pﬁster & Bradbury 1996). In 1977 there were 12 boats in the ﬂeet and this increased only slowly until 1986 when the ﬂeet approximately doubled each year until 1989, when there were 189 boats. The explosive growth of the ﬂeet prompted an emergency closure of the ﬁshery in 1988, when mid-season surveys in one zone suggested that legal-sized sea urchin density had declined by 63%. Following the peak season in 1988, the ﬁshing season was further restricted and a limited entry scheme reduced the ﬂeet by 67%. From 1988 until 1993, when a model-based quota system was established, managers made ad hoc adjustments to season length based on the observed trends in sea urchin density and size at index stations. In 1994, the U.S. Federal District Court granted 16 First Nation tribes access to half the annual sea urchin quota, but tribal ﬁshers did not enter the ﬁshery until 1996. Each of the 16 tribes was legally constrained to ﬁsh within “usual and accustomed” ﬁshing areas, preventing them from participating in the large-scale rotational ﬁshing system. As a consequence, the rotational ﬁshing scheme introduced in 1977 was abandoned. The green sea urchin ﬁshery was regulated from its beginning with ad hoc annual quotas. These ranged from a high of 455 t in 1987 to 227–272 t since 1992. A MLS of 57 mm was also imposed in 1987, based on an assumed size-at-maturity of 50 mm. Harvesting typically has been allowed earlier in the year than red sea urchin ﬁshing because of earlier maturation of green sea urchin gonads. During December and January both ﬁsheries are open.

Assessment and Status The most important data for the ﬁshery for red sea urchins is a time series of relative abundance indices (Pﬁster & Bradbury 1996, Lai & Bradbury 1998). Biomass at index 386

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stations was estimated using either a change-in-ratio estimator or an adaptive cluster sampling design. Surveys began in 1984 but the programme was reduced in 1995 and terminated in 1997 (Lai & Bradbury 1998). In 1993 and 1994, underwater video was used in the San Juan Islands to estimate the density of red sea urchins and sea cucumbers (Parastichopus californicus), using a stratiﬁed systematic sampling design (Bradbury et al. 1998). These surveys provided estimates of the relative abundance of sea urchins in three size classes (undersize, size and oversize with respect to the size limits). At the San Juan Islands, densities of legal-sized red sea urchins halved between 1988 and 1989; catch rates in the same area declined only about 10%. Changing diver behaviour, such as increasing depths for harvesting, and ﬁshing in new areas (Pﬁster & Bradbury 1996) make interpretation of these trends in catch rate difﬁcult. Nevertheless, the overall decline in catch rate suggests an overall reduction in abundance. The Washington ﬁshery has been assessed using a size-structured model that evolved from a relatively simple simulation model to an assessment model designed to provide management advice (Pﬁster & Bradbury 1996, Lai & Bradbury 1998). Pﬁster & Bradbury (1996) used a relatively simple size-structured model to explore the effects of assumptions about population processes such as constant recruitment and positive density dependence caused by fertilisation success and canopy sheltering behaviour noted above. The impacts of these assumptions were compared under deterministic projections of up to 100 yr. Including positive density-dependence in population processes caused signiﬁcantly greater declines in the modelled population under ﬁshing. Variable recruitment increased uncertainty considerably and only relatively light ﬁshing was sustainable. Under variable recruitment the period of rotation had to be longer than two years to maintain the population at greater than 50% of pre-ﬁshing biomass. Ebert (1998), however, subsequently disputed the importance of positive density-dependence in sea urchin population models, based on tagging studies in Washington and Oregon. Beginning in 1993, a size-structured model ﬁtted to survey data and population sizefrequency distributions has been used to recommend harvest rates in the ﬁve management areas (Lai & Bradbury 1998). The model results supported the earlier ﬁnding of Pﬁster & Bradbury (1996) that periodic rotation reduced yields but lowered the risk of stock collapse and year-to-year variability in yield. The model predicted that yields would be maximised for a three-year rotational system with a 20% harvest rate, and for an every-year ﬁshery with a 10% harvest rate. When Washington switched to an every-year ﬁshery, annual harvest rates ranged from 3–9% of the estimated harvestable biomass. These rates varied by zone based on the estimated proportion of the unﬁshed biomass remaining in a zone. In 1998, red sea urchin TACs were reduced by 15% from the 1997 levels as an arbitrary precaution in the absence of survey data. There have been no formal assessments of the green sea urchin ﬁshery. Although surveys done in 1993–94 to estimate the density of red sea urchins also counted green sea urchins, these estimates were believed to be unreliable for green sea urchins.

China Sea urchin ﬁsheries in China are concentrated in the Yellow Sea and, to a lesser extent, the South China Sea. Four species are harvested: Hemicentrotus pulcherrimus, Strongylocentrotus nudus, Anthocidaris crassispina and Glyptocidaris crenulatus. Of these, the second and fourth are the most important. The ﬁshery for G. crenulatus is restricted to the northern 387

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Yellow Sea where it is caught by dredge at depths between 20 m and 30 m. The remaining species are collected by hand in the intertidal zone, by diving, and in the shallow subtidal zone from small boats using rakes and spy-glasses. The ﬁshing season for G. crenulatus and Strongylocentrotus nudus is between November and June, and Hemicentrotus pulcherrimus are caught between October and December. There are MLS restrictions for H. pulcherrimus (30 mm) and for Strongylocentrotus nudus (50 mm). Few formal statistics are available for Chinese sea urchin ﬁsheries. FAO statistics report small catches of less than 300 t yr−1 for all echinoderms combined, but these are almost certainly underestimates of actual harvest. Japanese import statistics suggest there is a much larger ﬁshery. In 1999, for example, 382 t of roe was imported to Japan (see Table 1, p. 345). Since 1986, up to 50 t of Hemicentrotus pulcherrimus has been exported to Japan from Qingdao each year but, since 1989, there appears to have been a mass mortality of this species and catches have ceased. The combined catch of Strongylocentrotus nudus and Glyptocidaris crenulatus from the Yellow Sea reached 1200 t in 1995 with G. crenulatus accounting for 60% of this catch. Landings have subsequently declined, possibly because of over-ﬁshing and less than 50 t was reported landed in 2000.

Oregon (USA) The ﬁshery for red sea urchins (Strongylocentrotus franciscanus) began at Port Orford in 1986. Landings in 1988 were less than 26 t, but rose rapidly, reaching a peak of 4222 t in 1990. Since then landings declined rapidly to their 1999 level of 112 t (J. Schaefer, Oregon Department of Fish and Wildlife, pers. comm, Fig. 25). The high catch rates in the early years of the ﬁshery were maintained by divers moving to new grounds once the accumulated stocks had been harvested. The ﬁshery is now believed to be reliant on annual recruitment (Richmond et al. 1997).

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The ﬁshery currently operates from six main ports from Bookings to Depoe Bay, but is concentrated in the far south of the state, and Port Orford in particular. The ﬁshery is exclusively a dive ﬁshery – divers typically work from boats less than about 12 m in length using surface-supplied air. The ﬁshery was unregulated for the ﬁrst two years but participation was then limited to 92 divers, each with a non-transferable license. In 1988, a MLS of 3 in (76 mm) and mandatory logbooks were introduced. In 1991 the MLS was increased to 3.5 in (89 mm) following per-recruit modelling (Golden et al. 1991, as cited in Richmond et al. 1997). In subsequent years several closures were implemented, notably around colonies of Northern (Steller) sea lions, and ﬁshing was banned in very shallow waters (<10 ft deep). In 1995 an effort reduction scheme was implemented to reduce participation to 30 divers. Under this scheme three permits have to be cancelled for each new entrant. Catch rates declined from a peak in 1989 but have remained relatively stable since 1992 despite falling catches (Richmond et al. 1997). Similarly, the mean size of red urchins landed has declined and the proportion of large sea urchins has fallen sharply. Since 1994 poor gonad quality has changed patterns in ﬁshing effort, confounding interpretation of trends in catch and effort. Although it currently produces only a little over 100 t yr−1, the ﬁshery is believed to be stable. The management goal of 30 permits is believed to be sufﬁcient to ensure sustainable catches (Richmond et al. 1997). In 1991, the harvest of purple sea urchins (S. purpuratus) was allowed under a special permit. A MLS and other conditions governed harvesting. Area based quotas were set and areas were closed to ﬁshing when densities dropped to 30% of their pre-ﬁshing level (Richmond et al. 1997). Catches have always been small – they peaked in 1994 at 99 t or 11% of the total harvest but have since fallen to less than 2% of a declining total harvest. Several sea urchin reserves have been established as unﬁshed controls.

Australia A small commercial ﬁshery in Tasmania for the sea urchin Heliocidaris erythrogramma exports processed roe to the Japanese market (Dix 1977a,b, Sanderson et al. 1996, Keesing 2001). Harvesting is by diving only and the recreational and illegal ﬁsheries are thought to be negligible. Annual landings have been less than 260 t since the ﬁshery’s inception in 1986. Catch statistics are available only since 1990 and indicate few clear trends in either catch or catch rate over the last decade. Catches have increased in recent years, partially in anticipation of a management plan and further restrictions on participation. Roe recovery rates are low compared with other sea urchin ﬁsheries. For the 10 yr for which data are available, roe recovery averaged 4.3% (S.D. = 0.74) but has increased for each of the last seven years. It is unclear whether this trend is caused by reductions in the density of sea urchins, increased diver experience or processes unrelated to the ﬁshery. The only management in place in the ﬁshery has been a moratorium on the issue of permits, possession of which entitles a diver to harvest without restriction. In 1998, only 15 of the 60 permitted divers participated in the ﬁshery, so latent effort is of some concern. There have been no assessments of the status of stocks and there is little contrast in time series of catch rate (C. Johnson, unpubl. data). Trends in catch and effort are further confounded by poor data quality. A draft management plan proposes a MLS (Dix 1977b), closed areas and seasons, and effort controls.

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In New South Wales, two species are harvested in small but developing ﬁsheries (King et al. 1994, Andrew et al. 1998, Byrne et al. 1998). The large, fast growing sea urchin Centrostephanus rodgersii is abundant and an important herbivore on shallow subtidal reefs (Andrew & Underwood 1993, Andrew & O’Neill 2000, see Andrew & Byrne 2001 for review). Until recently there was an exploratory ﬁshery only for this species and catches were less than 5 t yr−1 (Andrew et al. 1998) but, in 2000, 23 t was harvested (C. Blount, New South Wales Fisheries, pers. comm.). Since 1998, a second species, Heliocidaris tuberculata, has been harvested, with more than 83 t landed in 2000 (C. Blount, pers. comm.). Sea urchin harvesting is limited to 37 divers who own transferable licences to catch sea urchins by diving. Harvesting is restricted to approximately half the coastline of New South Wales. There have been no stock assessments of either species.

France Sea urchin ﬁsheries in France are some of the oldest in the world (Allain 1972, Le Direac’h 1987, Le Gall 1987). In their modern form they principally supply markets in Paris and Marsielle (Le Gall 1987, Boudouresque & Verlaque 2001). The violet sea urchin, Paracentrotus lividus, dominates these ﬁsheries and has traditionally been harvested in Brittany as well as in the Mediterranean (Le Gall 1987, Le Direac’h 1987, Le Direac’h et al. 1987). Sphaerechinus granularis and Psammechinus miliaris are also harvested for human consumption in Brittany and in the Mediterranean (Le Gall 1987, Guillou & Michel 1993). Other species, such as Arbacia lixula and Echinus spp., are harvested to make curios for tourists (Sloan 1985, Le Direac’h 1987). Sea urchins have been harvested in France using a variety of methods, such as hooks, grapples, and hand-held drags, but diving is now the predominant method (Le Gall 1987, Le Direac’h 1987). In the Breton ﬁshery, a unique entanglement device, known as “le faubert” has been used to catch sea urchins since at least 1935 (Le Gall 1987). This method is considered to cause considerable incidental mortality of sea urchins as well as damage to the sea ﬂoor (Sloan 1985). A wide variety of management tools are used in French ﬁsheries, including size limits, closed seasons, gear restrictions and marine reserves. Reported landings in France peaked in 1945, when 1131 t was landed, almost all of it from the Mediterranean ﬁshery (Le Direac’h 1987). Catches have since declined considerably, and in 1998, only 59 t was recorded in the FAO summaries for these ﬁsheries (Fig. 26). For most of the 1950s and early 1960s the ﬁshery in Brittany accounted for 30–60% of national production before it collapsed in the late 1960s (Southward & Southward 1975, Sloan 1985, Le Gall 1987). During this time catches in northern Brittany fell from more than 300 t yr−1 in 1962 to less than 60 t in 1970, and subsequently to less than 30 t (Fig. 26). Guillou & Michel (1993) reported landings of 250 t of Sphaerechinus granularis from the Glénan Islands in southern Brittany but these do not appear in the FAO summaries. Reported landings from the Mediterranean have declined over a longer time period from a peak of 1106 t in 1945 to less than 60 t in 1998 (Le Direac’h 1987) (Fig. 26). The abundance of sea urchins in the western Mediterranean, particularly around Marseille, appears to have ﬂuctuated widely in response to ﬁshing (both of sea urchins and, conversely, their predators), disease, and pollution (see papers in Boudouresque 1987 and Boudouresque & Verlaque 2001 for review). The complexity of these processes and the absence of stock assessments make inferences about the current status of stocks difﬁcult (Sala et al. 1998a, Boudouresque & Verlaque 2001). 390

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Ireland Paracentrotus lividus is common throughout rocky intertidal and shallow subtidal zones of southern Ireland and has been exported to France since at least 1948 but catches were relatively small until the Breton ﬁshery collapsed (Southward & Southward 1975, Sloan 1985). Recorded landings peaked in 1976 at just over 350 t (Fig. 27) but landings were probably substantially under-reported (Moylan 1997). Landings have subsequently declined and in 1999 only 3.4 t was reported (Fig. 27 and Barnes et al. 1999). Populations of P. lividus have been severely depleted in many areas of southern Ireland (Byrne 1990, Moylan 1997). Many factors may have contributed to the demise of the Irish ﬁshery, including long-term variation in recruitment, but the most likely and predominant cause is over-ﬁshing. Through 391

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the 30 yr that the ﬁshery expanded, peaked and declined there were no government imposed constraints on catch or effort (Moylan 1997), there were no formal assessments of stock status and no assessments of the recovery of populations depleted by ﬁshing (Moylan 1997). Market demand for sea urchins larger than 50 mm provided the only apparent constraint on harvesting (Sloan 1985, Moylan 1997).

Iceland A ﬁshery for the green sea urchin Strongylocentrotus droebachiensis, developed in Iceland in the early 1990s, primarily for the Japanese market (Einarsson 1994). The ﬁshery had an explosive development, increasing from negligible amounts in 1992 to a peak of 1500 t in 1994. In the two following years total catch declined to 500 t in 1996 and 20 t in 1997, with negligible catches since (Einarsson 1994, S. E. Einarsson, unpubl. data). The ﬁshery began as a dive ﬁshery but quickly changed to using a modiﬁed scallop dredge. The ﬁshery declined precipitously, not because of over-exploitation but because of poor demand (Einarsson 1994). During the brief life of the ﬁshery there were no management restrictions on effort or catch. The sustainable annual harvest of sea urchins is thought to be 1600 t (Einarsson 1994).

Other ﬁsheries In addition to those described above, small ﬁsheries exist in Barbados, Fiji, Peru, and North Korea, among other places. Information on these ﬁsheries comes predominantly from FAO statistics. Based on 1998 FAO data these ﬁsheries collectively account for less than 2% of world production. It is likely that much of the production of echinoderms in Fiji (503 t in 1998) was holothurians (Conand 2001) but there is insufﬁcient information available to separate these taxa. Sloan (1985) reported a substantial ﬁshery for Anthocidaris crassispina in Hong Kong (2000 t in 1980) but there is little information available on this ﬁshery and it is not included in estimates of world production. Paracentrotus lividus has been harvested from Portugal, Morocco, and in the Mediterranean Sea for many centuries (Sloan 1985, Le Direac’h et al. 1987, Le Gall 1987, Boudouresque & Verlaque 2001) but there is little published information on the current status of these ﬁsheries. There are also small artisanal ﬁsheries, particularly in the tropics, that remain poorly described in the international literature. In Barbados there is a ﬁshery for Tripneustes esculentus with large ﬂuctuations in catch (Lewis 1958). Scheibling & Mladenov (1987) reported that the ﬁshery had collapsed but more recent reports suggest it has recovered (Vermeer et al. 1994). At the Galapagos Islands an artisanal ﬁshery takes T. depressus and there is considerable pressure to develop this into a commercial ﬁshery as other near-shore ﬁsheries, particularly those for holothurians, decline (P. Guarderas, pers. comm.). In North Korea, FAO reports only a small ﬁshery that began in 1986 and peaked a year later at 250 t. There are also developing ﬁsheries in several eastern provinces in Canada, all for Strongylocentrotus droebachiensis. There are large resources of, but small ﬁsheries for, sea urchins in Labrador and Newfoundland (R. Hooper, Memorial University, pers. comm.). The ﬁshery in Newfoundland is at present expanding quickly and may develop into a major ﬁshery (R. Hooper, pers. comm.). Largely unexploited resources also exist in Quebec 392

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(Himmelman et al. 1983, Bonardelli 1997, J. Himmelman, pers. comm.), Norway (Hagen 1987), and the Gulf of Mexico (Watts et al. 2001) among other places.

Enhancement Enhancement of sea urchin ﬁsheries may be divided into three categories: (a) reseeding, (b) habitat enhancement, and (c) transplantation in wild populations. We deﬁne “reseeding” as the release of hatchery-reared juveniles to augment natural recruitment. The term “sea ranching” is commonly used in the Japanese context to refer to such reseeding (e.g. Imamura 1999) but we reserve that term for husbandry of animals under semi-controlled conditions in the wild. Partially in anticipation of the demise of wild ﬁsheries, aquaculture and sea ranching have become active ﬁelds of research (see the papers in Journal of Shellﬁsh Research 17 (8) for a recent collection). Roe enhancement in wild populations involves transplanting wild juveniles or adults from habitats where somatic and gonadal growth are low to other habitats where they are higher. Adult sea urchins may also be transplanted to establish populations of sea urchins in areas that have been denuded. Although we brieﬂy review the enhancement of wild populations by transplantation, sea ranching and land-based aquaculture are beyond the scope of this review.

Reseeding The theoretical basis of stock enhancement by reseeding is the belief that populations are recruitment limited (Doherty 1999) (i.e. abundance is limited by processes acting on sea urchins before they settle and grow large enough to be sampled in the ﬁeld). Such processes may include fertilisation success, food limitation, predation of larvae before and during settlement, and dispersal. Circumventing mortality in these early life-history stages by outplanting hatchery-reared juveniles holds the potential to improve on nature. Travis et al. (1998) put the argument neatly: The appeal of stock enhancement rests in its simple premise and its bold promise. The premise is that we can raise large numbers of larvae or juveniles and, by releasing them successfully into the marine environment, compensate for the enormous natural mortality in these stages and thereby increase stock size in the late juvenile and early adult stages. The promise is that this intervention will compensate for the ﬁshing mortality that created the problem in the ﬁrst place. Successful reseeding rests on the further assumption that the population receiving the out-planted animals is not near the carrying capacity of the environment. Theory contends that density-dependent processes, particularly food limitation, get stronger as a population approaches the carrying capacity. For reseeding to make sense, mortality of juveniles has to be relatively independent of density, at least within the range observed in the population receiving reseeded juveniles. If this were not the case then the reseeded sea urchins would simply “replace” those already in the wild population. There is relatively little evidence of density-dependent mortality in edible sea urchins (Lawrence 2001) and consequently the notion 393

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of an environmental carrying capacity for sea urchins is a little elusive. Sea urchins share attributes with other echinoderms (e.g. the lack of a large muscle mass) that buffer individuals from density-dependent mortality (Johnson & Mann 1982, Andrew 1989). These generalisations suggest that sea urchins may be appropriate candidates for enhancement by reseeding. Enhancement of sea urchin ﬁsheries, particularly through reseeding, has reached its fullest expression in Japan where it has been a major management tool for more than a decade (Saito 1992, Imamura 1999). The number of sea urchins reseeded in Japan increased sharply in the late 1980s and has plateaued since 1994 (Fig. 28). In 1996, 78 464 million sea urchins were reared in hatcheries and reseeded into the wild. Strongylocentrotus intermedius accounts for 84% of these, most being released in Hokkaido. Other species reseeded were: S. nudus, Pseudocentrotus depressus, Hemicentrotus pulcherrimus, Anthocidaris crassispina, and Tripneustes gratilla. Of these, only the reseeding programmes for Pseudocentrotus depressus and Hemicentrotus pulcherrimus are of any magnitude. Between 1980 and 1998, the number of Tripneustes gratilla and Anthocidaris crassispina out-planted rarely exceeded 200 000 yr−1 and there were many years when there were none. The effectiveness of the Japanese reseeding programme has not been evaluated on a national or prefectural scale (Saito 1992, Kitada 1999). In South Korea, reseeding has only recently begun; 700 000 juvenile A. crassispina and Strongylocentrotus intermedius of about 10 mm in size are now produced each year at national hatcheries and released onto reefs on the east coast (National Fisheries Research and Development Institute 2000). No information on the effectiveness of this programme is available. Of the remaining 16 ﬁsheries that have produced more than 1000 t yr−1 at some stage in their history, only in the Philippines has reseeding research been scaled up to be a tool used in management. In Bolinao, on northern Luzon Island in the Philippines, reseeding and grow-out of Tripneustes gratilla has been conducted since 1993 (Juinio-Meñez et al. 1998). This has 394

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been done both to re-establish a viable population after severe over-ﬁshing and to provide a source of sea urchins for aquaculture. Since 1997, 40 000–80 000 juveniles (>10 mm) have been produced per year for reseeding or for small-scale grow-out culture. Since 1998, ﬁshers have developed systems for holding sea urchins in cages on the reef ﬂat and at any one time, 6000 to 10 000 sea urchins are growing in these cages. The sea urchins are not harvested until they reach at least 70 mm and have the opportunity to spawn several times before being harvested, so the grow-out cultures function as reproductive reserves (Juinio-Meñez et al. 1998). In addition, selected reef areas, including some within established marine reserves have been reseeded since 1996 (Juinio-Meñez et al. 1998). Populations of sea urchins on these reefs have increased signiﬁcantly in comparison with those in nearby areas. To aid development of management policies for the Philippine enhancement programme, research is determining the genetic structure of T. gratilla populations along the western coast of Luzon. Numerical larval dispersal models indicate that the sea urchin populations in this region are not self-seeding and are regularly mixed due to hydrographic regimes associated with monsoons (Juinio-Meñez & Villanoy 1995). This is corroborated by initial allozyme studies which indicate high levels of gene ﬂow between populations in this region (Malay et al. 2001). Evaluating the success of reseeding sea urchins is difﬁcult because, unlike abalone and other reseeded species (e.g. Kojima 1995, Kitada 1999), sea urchins show no discernible differences between reseeded and wild individuals that can be detected in sampling programmes. To our knowledge, no methods to tag sea urchins externally have been developed that do not carry a signiﬁcant risk of increased mortality and growth, both of which would bias estimates of the proportion of reseeded animals in the catch (Ebert 2001). Evaluating the effectiveness of sea urchin reseeding programmes where there are signiﬁcant wild populations therefore presents signiﬁcant methodological challenges. Internal tagging using PIT tags (Hagen 1998) and chemical tags such as tetracycline and calcein may offer the only immediate way forward. More fundamentally, the logical framework for reseeding programmes needs to change to be more experimental – size or growth differences between seeded and wild sea urchins in experimental areas and unseeded controls could provide a test of the effects of reseeding (Hilborn 1998, Leber 1999). In California, the success of small-scale reseedings of Strongylocentrotus franciscanus has been evaluated using cultured juveniles marked with calcein (Ebert et al. 1992, Dixon et al. 1997). An experimental seeding of 5000 individuals at each of four sites indicated that ﬁrst year mortality was extremely spatially variable and strongly size-dependent. Based on complete collections from large areas around the transplant sites, minimum survival at the four sites varied from 0% to 0.3% for 5-mm sea urchins, 0% to 6.8% for 10-mm sea urchins, and from 1% to 22% for 15-mm individuals. In a second experiment, 10 000 calcein tagged juveniles (mean size 19 mm; range, 12–30 mm) were released at two sites. At one site there was catastrophic mortality (>99%) in the second half of the ﬁrst year, possibly due to asteroid predators (Pycnopodia helianthoides). At the second site, 19% of seeded animals survived their ﬁrst year. Growth was similar to that observed in wild populations (Ebert et al. 1999), and seeded animals began entering the ﬁshery after 3 yr. The modal size of the seeded urchins reached the MLS after 5 yr in the ﬁeld. The proportion of seeded animals in controlled commercial harvests in the immediate vicinity of the seeded area varied from 6% after 3 yr to 26% after 5 yr. Based on exhaustive searches, about 10% of seeded sea urchins survived for 5 yr (J. Dixon, S. Schroeter & T. Ebert, unpubl. data). As a result of extremely high early mortality, slow growth, and the expense of culturing animals to an effective 395

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seeding size of 15 mm or more, reseeding with hatchery-reared juveniles will probably not be economically feasible for most Strongylocentrotus franciscanus ﬁsheries.

Habitat enhancement The aim of habitat enhancement programmes is to expand areas of good habitat for sea urchins and promote colonisation of algae as food (Morikawa 1999). In Japan, stones and/or large cement blocks have been introduced in a number of prefectures to increase the area of appropriate habitat and provide more shelter, for example in Fukui Prefecture (Taki & Higashida 1964), and in Hokkaido (Kawamura 1973, Agatsuma 1991; see also Mottet 1976, as cited in Tegner 1989). In South Korea, 7.6 cu.km of artiﬁcial reefs have been established in 151 649 ha of ﬁshing grounds since 1971. These reefs are mostly large concrete structures, although steel is being increasingly used, and are made up of modules of 8 m3 each. The reefs have been established on all coasts but particularly on the east and south coasts. Since 1990, artiﬁcial reefs have been developed speciﬁcally for shallow-water species (<10 m depth), particularly abalone, topshells, algae and sea urchins. These reefs are designed to enhance village-based ﬁsheries and now account for approximately 30% of the reefs established each year. Despite the 30-yr history of this enterprise, the effects on coastal ﬁsheries and ecosystems have not been analysed in any detail.

Transplantation The weight of sea urchins caught is only one determinant of the value of the harvest. The time of year, and food quantity and quality also interact to determine size and quality of roe. Nutrients are stored in somatic cells within the gonad before being utilised in body growth or reproduction (Giese 1966, Holland et al. 1967). Most urchins harvested in the world are consumed in Asian markets and are most valuable prior to and in the early stages of gametogenesis when their roe are large but still ﬁrm in texture. Their value diminishes when sufﬁcient gametes in the lumen of the gonad change the texture of the roe. Roe enhancement in wild populations is most commonly achieved by transplanting adult sea urchins from areas with poor gonad development to kelp forests (Tegner 1989 and see below). Moylan (1997) reports that ﬁshers on the west coast of Ireland moved sea urchins to increase roe size. In most other ﬁsheries, e.g. Mexico (J. Palleiro, pers. obs.) and California (Tegner 1989), such work remains at a research scale and has not been commercialised. Sea urchin gonads can readily increase in size over a 3–4 month period at any time of year (e.g. Andrew 1986, Klinger et al. 1997, Vadas et al. 2000) but are most effectively manipulated in the months when nutrients are beginning to be mobilised for gametogenesis. In Japan, somatic growth and gonadal development have been signiﬁcantly improved by transplanting adult sea urchins from deep water or barrens habitat in most commercial species: S. intermedius and S. nudus (Kawamura 1965, 1966, Yano et al. 1994, Agatsuma 1997, Kawamata 1998), Hemicentrotus pulcherrimus (Kawana 1938), Pseudocentrotus depressus, and Anthocidaris crassispina (Nakamura & Yoshinaga 1962). It is possible to increase roe size to 18% of body weight (the minimum considered commercially viable) in two months in aquaria, but usually this takes three months in the ﬁeld (Agatsuma 1999). 396

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In California, the efﬁcacy of transplantation as a means of enhancing the ﬁshery for Strongylocentrotus franciscanus has been tested in a co-operative project with ﬁshers. About 33 000 small sea urchins were transplanted from barrens habitat to a kelp forest which had only a sparse population of mostly large sea urchins (Dixon et al. 1999). Survival and growth of the transplanted sea urchins were high. Thirteen months later, an estimated 58% (95% CI: 28–87%) of the transplanted sea urchins were still alive and these animals grew 15 mm in that time. This is close to the maximum growth rate observed for red sea urchins between Alaska and San Diego, California (Ebert et al. 1999). Removal of approximately a third of the local population at the source site had no detectable effect on population density 13 months later, largely because of a extremely high recruitment there. Transplanted sea urchins had little effect on the two dominant species of large brown algae; the mean density of Macrocystis pyrifera increased slightly and Eisenia arborea declined by approximately 20% in the year following the transplantation (Dixon et al. 1999). Dixon et al. (1999) concluded that transplantation of naturally occurring small sea urchins is a viable alternative to the more costly technique of reseeding hatchery-reared juveniles (see also Nova Scotia, p. 379). Areas of barrens habitat often do not support sea urchins of a marketable size or gonadal quality, but could provide a potentially large supply of animals for transplantation. Balancing these beneﬁts are the possible adverse effects of overgrazing at transplant sites and of increasing rates of mortality by moving sea urchins from areas of barrens habitat which serve as de facto refuges from harvesting. Dixon et al. (1999) suggest that, given the high temporal and spatial variability in sea urchin recruitment, it is important to repeat commercial-scale transplantations to determine the costs and beneﬁts during periods of below average recruitment.

Ecological effects of ﬁshing There are numerous examples in the literature of large-scale changes in the ecology of reefs as a result of harvesting ecologically important species, including sea urchins, ﬁshes and predatory whelks. Examples may be found in many regions, for example, on rocky reefs in the Mediterranean (Sala et al. 1998a, but see Sala et al. 1998b), the northeast Paciﬁc (Simenstad et al. 1978), Gulf of Maine (Witman & Sebens 1992, Vadas & Steneck 1995), Chile (Castilla & Moreno 1984, Duran & Castilla 1989), Australia (Andrew et al. 1998), and on coral reefs in the Caribbean (Hughes 1984) and Kenya (McClanahan & Shaﬁr 1990, McClanahan 1997). Regardless of the value judgements placed on changes in ecosystems, large-scale commercial sea urchin ﬁsheries may have complex ecological effects, possibly disproportionate to the number removed (e.g. Andrew & Underwood 1993). Below, we brieﬂy review the ecological effects of ﬁshing for sea urchins and associated species (see also Steneck 1997, Tegner & Dayton 2000). The management responses to such effects are considered on pp. 402–405).

Effects of harvesting sea urchins Most sea urchins harvested in the world’s ﬁsheries are hand-collected by divers or taken in the intertidal zone. In such ﬁsheries the effect of harvesting is mediated through changes in 397

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the relative strength of interactions rather than direct damage to the sea ﬂoor (Dayton et al. 1998, Tegner & Dayton 2000). Many species of sea urchin play important roles in the ecology of subtidal reefs; this literature has been exhaustively reviewed, most recently by Lawrence (2001), and will be only cursorily treated here. Experiments have repeatedly demonstrated strong effects of removing sea urchins but few studies of indirect effects have actually studied ﬁsheries; inferences are usually drawn from the ecological literature and are based on small-scale experiments or the effects of catastrophic, large-scale reductions in sea urchin abundance. The greatest impact of removing many thousands of tons of sea urchins from temperate reefs is usually the rapid development of stands of large brown algae and consequent changes in the relative abundance of ﬁshes and benthic invertebrates (see reviews in Lawrence 2001). The ecological effects of the ﬁshery in Maine are better understood than in most. Sea urchins are dominant grazers on shallow reefs (e.g. Ojeda & Dearborn 1989, Steneck & Dethier 1994, Steneck 1997, Scheibling et al. 1999). As the abundance of sea urchins has declined, the formerly widespread barrens habitat has increasingly been replaced by kelp forests (Vadas & Steneck 1995, McNaught & Steneck 1998). McNaught and co-workers have demonstrated that post-settlement mortality, thought to be caused by predation, signiﬁcantly reduces recruitment of sea urchins within kelp forests (McNaught & Steneck 1998, McNaught 1999, see also Balch & Scheibling 2001). Thus, the development of kelp forests following intense ﬁshing reduces recruitment and the productivity of the ﬁshery. Reemergence of kelp forests as a dominant habitat also causes changes in local distribution patterns of other species, such as ﬁshes (Levin 1994) and lobsters (Bologna & Steneck 1993), which use kelp forests as shelter. In Baja California there have been large increases in the purple sea urchin (Strongylocentrotus purpuratus) following ﬁshing of its congener, S. franciscanus (J. Palliero, unpubl. data). This change was predicted by Schroeter (1978) who found that red urchins were competitively superior. In New Zealand, harvesting Evechinus chloroticus causes an increase in the abundance of large brown algae (McShane et al. 1994, Villouta 2000, Villouta et al. 2001). From research on an experimental ﬁshery in Fiordland, Villouta (2000) suggested a threshold of 2–3 sea urchins m−2, below which large brown algae are able to reestablish themselves and a large change in community structures follows. The effect of harvesting E. chloroticus on other invertebrates and ﬁshes is poorly understood. In southern California, the ﬁshery for Strongylocentrotus franciscanus interacted with large-scale oceanographic events associated with the 1982–84 El Niño to mitigate the effects of sea urchin grazing (Tegner & Dayton 1991). Warm water associated with the 1982–84 El Niño reduced recruitment and consequently, the episodes of over-grazing by sea urchins that characterised earlier El Niños did not occur. In northern California, the red sea urchin ﬁshery has had a positive effect on kelp forests and abalone. Aerial photographs during the period of intense sea urchin ﬁshing, showed a dramatic increase in the surface canopy from 1982 to 1989. During this period, red abalone (Haliotis rufescens) grew faster and there were more large abalone present than before the sea urchin ﬁshery began (Karpov et al. in press). In France, Japan, Maine, New Brunswick and New Zealand a small proportion of the total catch is taken by dredging or dragging; in these ﬁsheries harvesting may have a direct effect on the ecology of reefs. This has been studied in Maine and New Brunswick where dragging is used to harvest sea urchins from areas of consolidated cobbles and low-lying rock ledges to depths of 30 m. Sea urchins, as well as other invertebrates and kelps may be abundant in this habitat type (Ojeda & Dearborn 1989). In Maine, a variety of dragger 398

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designs are used and together they account for approximately 20% of the total landings. The heaviest design is a modiﬁed scallop dredge or “chain sweep” which is used in eastern Maine and in areas where the currents are stronger. Other designs such as “pipe drags” and “whale mouth drags”, have been developed speciﬁcally for sea urchins; these have a lighter construction and are increasingly popular (Creaser & Weeks 1998, Wahle 1999). The underside of a drag consists of chains arrayed in a rectangular grid or web and the sea urchins are “plucked” up through this web. Wheels and pipes in the front of the drag bounce it over boulders and other impediments. Wahle (1999) described the short-term effects of dragging on cobble substrata and rocky reefs in Maine. Three drag designs were compared in an experiment in which ﬁve passes of a commercial dragger over ﬁxed areas of substratum were compared with control areas. The majority of the catch (excluding rocks) in all three designs was sea urchins. Cobbles and boulders, as well as other by-catch accounted for a greater proportion of the total catch using the “chain sweep” drag. In all three designs, algae was only a minor proportion of the catch. The “chain sweep” design caused signiﬁcant reductions in the abundance of large brown algae, but not in infaunal species or species diversity. The lighter “pipe” and “whale mouth” drags caused less damage, mostly to large kelp (Laminaria spp.) and did not signiﬁcantly reduce species diversity or the abundance of infauna. Long-term effects and a consideration of dragging intensity were beyond the scope of Wahle’s (1999) study but it is likely that repeated dragging will have more signiﬁcant long-term effects. In New Brunswick, two sites were experimentally ﬁshed to examine the impacts of scallop drags used to harvest sea urchins (Robinson et al. 2001). Immediately after ﬁshing there was a signiﬁcant decline in sea urchin densities and an increase in the number of broken sea urchin tests. Lobsters were absent from the dragged reef immediately after ﬁshing but had returned within three months. Whelks, crabs and sculpins were more abundant in the dragged areas immediately after ﬁshing, probably in response to the disturbance. The breakage rate of the kelp, L. longicruris increased over the course of the dragging operation. Although there were short-term impacts from a single dragging event, the observable effects on the reef were gone in less than 3 months. The longer-term effects of repeated dragging are unknown.

Effects of other ﬁsheries on sea urchins Other ﬁsheries may affect the abundance of sea urchins and consequently the ecology of rocky reefs. Several case histories exist, notably sea otters in the northwestern Paciﬁc (see below), lobsters in Nova Scotia and Maine (Scheibling 1996, Scheibling & Hatcher 2001), sheepshead and lobsters in California (Cowen 1983, Tegner & Levin 1983, Dayton et al. 1998), ﬁshes in the northwestern Mediterranean (Verlaque 1984, McClanahan & Sala 1997, Sala et al. 1998a,b), and lobsters and ﬁshes in New Zealand (Babcock et al. 1999). In all of these instances, harvesting predators is suggested to have precipitated an increase in the abundance of sea urchins and the consequent community changes associated with high densities of sea urchins. The underlying strength of evidence for these inferences is strongest for sea otters. The ecological impacts of removing predators on sea urchin populations has been exhaustively reviewed – here we conﬁne our discussion to the effect of other ﬁsheries on sea urchins. The increasing abundance and geographical range of sea otters (Enhydra lutris) presents a signiﬁcant threat to sea urchin ﬁsheries in Russia, Alaska, British Columbia, Washington, 399

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Oregon and California (Estes & VanBlaricom 1985, Reidman & Estes 1990, Watson et al. 1996). Sea otters were once widely distributed around the northern rim of the Paciﬁc Ocean from northern Japan to Baja California (Estes & VanBlaricom 1985, Estes & Duggins 1995). Intensive harvesting through the eighteenth and nineteenth centuries had devastating effects and reduced the distribution of sea otters to a few remnant populations within this range (Pitcher 1989). In the wake of this hunting the abundance of sea urchins increased in areas that previously supported sea otters (VanBlaricom & Estes 1988). Processes that determine the abundance of sea urchins outside the range of sea otters remain less well understood (Foster & Schiel 1988). The range expansion of sea otters has partly resulted from active management policies. For example, between 1965 and 1969, the Alaska Department of Fish and Game in cooperation with the U.S. Fish and Wildlife Service, transplanted 402 sea otters to six areas in southeast Alaska from the Aleutian Islands and Prince William Sound (Pitcher 1989). There are now more than 10 000 sea otters in the region (Woodby et al. 2000) and their effect on sea urchin populations may be seen near Sitka. Sea otters have been locally common there since the early 1990s when they began expanding their range southward along Baranof Island into sea urchin ﬁshery areas (D. Woodby, unpubl. data). A survey in 1993 indicated a 64% decline in sea urchin abundance. Because of the presence of sea otters and many cracked tests, this was attributed largely to sea otter predation. Further observations suggest that sea urchins remain at very low numbers in the area. The arrival of sea otters at other ﬁshed sites, such as Whale Bay on Baranof Island, and near Cape Chacon on Prince of Wales Island, during the 1990s resulted in similar reductions of sea urchin populations and the loss of these areas from the ﬁshery. Given the recent history of sea otter expansions, the future of the red sea urchin ﬁshery in Alaska is poor. In yet another twist in the sea urchinotter story, sea otters have again declined in numbers at the Aleutian Islands; Estes et al. (1998) attribute this decline to increased predation by killer whales (Orcinus orca). In Russia, sea otters pose the greatest threat to the sea urchin ﬁshery on the Kamchatka Peninsula where they have effectively removed all large sea urchins from shallow reefs within their range. Their distribution is expanding north from Lotka Cape. Between 1985 and 1988 the northern limit of their distribution was near Utashud Island, but this boundary has since moved more than 80 km northward, to Asatcha Inlet. As a result of this range extension, the density of sea urchins in water less than 10 m deep has declined considerably and the maximum size has halved (A. Bazhin, unpubl. data). Associated with these declines in sea urchin density, forests of the kelp Alaria ﬁstulosa have appeared. The appearance of sea otters in Listvenichnaja and Russkaja Inlets, in 1995, caused a collapse of populations of sea urchins there from 1.21 kg m−2 to 0.19 kg m−2 (A. Bazhin, unpubl. data). In British Columbia, sea otters were re-introduced to one locality between 1969 and 1972 and have subsequently colonised new areas, increasing in number at about 18.6% per annum (Watson et al. 1996). Sea otters are now found on the northwest coast of Vancouver Island and the central portion of the British Columbia coast (Watson & Smith 1996, Watson 2000). As in other areas colonised by sea otters, populations of sea urchins as well as abalone and clams such as geoducks (Panopea abrupta) are greatly diminished. As sea otters continue to increase in numbers and range they will increasingly compete with invertebrate ﬁsheries. In British Columbia the threat is greatest for red sea urchins because of the great overlap in the ranges of the two species, particularly north of Vancouver Island (Watson & Smith 1996). The ﬁshery for green sea urchins is concentrated in more sheltered waters inside Vancouver Island. Sea otters are considered threatened under both Provincial and Federal laws and are 400

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a protected species in British Columbia. At present there is no plan to manage sea otters nor to co-manage these interacting species. Other ﬁsheries, such as those for lobsters and predatory ﬁshes may also have impacts on sea urchin abundance but the evidence is less compelling than for sea otters. The effect of harvesting lobsters and predatory ﬁshes in the Gulf of Maine and Nova Scotia has been a matter of contention for nearly thirty years (Mann & Breen 1972, Elner & Vadas 1990, see Chapman & Johnson 1990, Scheibling 1996 for reviews). Witman & Sebens (1992) and Vadas & Steneck (1995) concluded that the large populations that now support the sea urchin ﬁshery in the Gulf of Maine are the result of over-ﬁshing for cod, wolﬁsh and haddock. This conclusion was based on studies conducted along an inshore to offshore gradient. Nearshore reefs and islands generally contained fewer and smaller sized ﬁsh, high densities of sea urchins and sparse populations of kelp, whereas offshore islands contained large macroalgal populations, few or no sea urchins and an abundance of large predators. Sea urchins that were tethered along the gradient were taken by ﬁsh predators at higher rates on offshore islands than on near-shore reefs. The reduction of large predatory ﬁsh in nearshore areas is thought to have occurred primarily during the last three centuries (Aronson 1990, Witman & Sebens 1992, Vadas & Steneck 1995). Explorations of Indian middens have revealed an abundance of large ﬁsh (>90 kg), mostly cod. These ﬁsh were probably caught in estuarine-coastal waters and were members, at least seasonally, of nearshore trophic webs. It likely that colonial and more recent ﬁshing activities had a positive impact on nearshore sea urchin populations in the Gulf of Maine. The progressive disappearance of communities dominated by large brown algae in the genus Cystoseira in the Mediterranean began in the 1960s and 1970s (Verlaque 1984). The decline of these communities seems to be due both to increased coastal eutrophication (Hoffmann et al. 1988, Cormaci & Furnari 1999) and the over-exploitation of sea urchin predators, which allows sea urchins to increase in numbers and ultimately to over-graze Cystoseira meadows, transforming them into areas of barrens habitat (Verlaque 1984, Sala et al. 1998a,b). Abalone co-occur with sea urchins in many temperate regions and the relationships between co-occurring species pairs appear to vary from competition to mutualism. In California, South Africa and Japan juvenile abalone shelter under the spine canopy of sea urchins (see Tegner & Dayton 2000 for review) and removal of sea urchins causes a decline in local abundance of abalone. In New South Wales, Australia, sea urchins competitively displace abalone (Andrew & Underwood 1993, Andrew et al. 1998). When sea urchins were removed from reefs to simulate ﬁshing, the abundance of abalone increased dramatically over a 3-yr period. In California red abalone (Haliotis rufescens) co-occur with red (Strongylocentrotus franciscanus) and purple (S. purpuratus) sea urchins and may compete for food and shelter (Tegner & Levin 1983, Karpov et al. in press). Abalone and red sea urchins are segregated in space with the former being more abundant in areas with a high biomass of large brown algae. Evidence for competition comes from Karpov et al.’s (in press) comparisons of abundance and size-structure between ﬁshed and unﬁshed reefs in northern California. The reduced abundance of red sea urchins, and subsequent increase in biomass of large brown algae might further increase abalone abundance through increases in food availability (Karpov et al. 1998). Macroalgae are harvested in several regions where sea urchins are harvested (Vásques 1995) but little is known of the effect of this harvest on sea urchin ﬁsheries. In California and Baja California, Macrocystis pyrifera is harvested, but this is thought to have little effect 401

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Figure 29

Total catch (t) of sea urchins and total algal production (t) in South Korea.

on the biomass of M. pyrifera or on the associated community (Barilotti et al. 1985, Dayton et al. 1998). In South Korea, macroalgae, Laminaria spp., as well as a number of species of smaller algae are harvested on a large scale (Fig. 29). The rise and decline of the ﬁshery for macroalgae is correlated with patterns in the sea urchin ﬁshery; both have been in decline since 1987. Whether the rise and fall of the sea urchin ﬁshery is linked to similar patterns in algal harvest is unknown.

Management Single species assessment Classical ﬁsheries theory contends that an unﬁshed population is constrained by the carrying capacity of its environment and exists at some dynamic equilibrium about that population size. The productivity of a ﬁshed stock is greatest at some lesser population size (Russell 1931, Schaefer 1954) and in the still-common logistic formulation of population growth, the maximum sustainable yield is taken when a stock is approximately half its unﬁshed biomass (Gulland 1971, Hilborn & Sibert 1988, Hilborn & Walters 1992). Populations decline under ﬁshing as the accumulated biomass of older and larger animals is removed. This pattern of “ﬁshing down” is typically reﬂected in large catches during the development phase of a ﬁshery (Perry et al. 1999). Critically, yields taken during the “ﬁsh down” phase are often far greater than those that can be sustained in the long term. Declining abundance does not necessarily mean that ﬁshing has diminished the population’s capacity to replenish itself and judging the population size at which this may occur is a central preoccupation of ﬁsheries science (Hilborn & Walters 1992, Hilborn et al. 1995, Myers et al. 1999). 402

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Catches in many large sea urchin ﬁsheries have declined and are now only a small fraction of what they were at their peak. Because these declines are usually not attributable to management, it may be inferred that the period of “ﬁsh down” in these ﬁsheries is either well and truly over or in an advanced stage. Assessments (see earlier ﬁshery summaries) suggest many ﬁsheries or parts thereof are over-ﬁshed, particularly in California, France, Maine, Ireland, Japan, South Korea and Washington. For others the evidence is equivocal (e.g. Mexico). Still others appear to be either relatively stable (e.g. British Columbia) or developing (e.g. Alaska, New Zealand); for some it is simply impossible to tell (e.g. Chile). The general pattern in sea urchin ﬁsheries is one of serial depletion of different areas within the ﬁshery, followed by declines and sometimes collapse over periods of years to a decade. An exception to this overall pattern of short-term “boom and bust” is the much longer-term decline in the Japanese ﬁshery, despite large efforts to enhance populations with releases of juveniles, closures, size limits and exclusive access rights held by ﬁshing co-operatives. Stock assessments have been reported in the literature for only a few ﬁsheries, notably Alaska, British Columbia, California and Washington. Surplus production methods are used in most instances (see ﬁshery summaries for more details). These techniques, developed in the 1950s for ﬁnﬁsh, are commonly used in ﬁsheries that are poorly understood, have poor datasets, or are developing (Garcia et al. 1989, Breen & Kendrick 1998, Perry & Waddell 1999). Underlying their application to sea urchin ﬁsheries is an assumed relationship between biomass and sustainable yield. In all applications to sea urchin ﬁsheries, conservative assumptions are made about biomass and, furthermore, the estimate sustainable yield is scaled by a “conservation factor” (Garcia et al. 1989). Management advice that ﬂows from these assessments is consistent with the precautionary approach to harvest policy currently advocated for developing invertebrate ﬁsheries (e.g. Walters & Pearse 1996, Walters 1998, Perry et al. 1999).

Allee effects Allee effects may have important consequences for managing sea urchin ﬁsheries. In contrast to the compensatory dynamics assumed in assessments of many ﬁsheries, declining densities may produce signiﬁcant depensatory responses (Levitan & Sewell 1998). In sea urchins, these effects may take several forms: (a) a minimum adult density necessary for successful spawning (Pennington 1985, Levitan et al. 1992) and (b) in red sea urchins, juvenile refuge from predation under adult spines (Bernard & Miller 1973, Tegner & Dayton 1977, Breen et al. 1985, Sloan et al. 1987). Although strong Allee effects may be present, this does not of itself mean that such effects are important to the dynamics of populations and the management of ﬁsheries. Population bottlenecks later in life may swamp any relationship between density of spawning animals and the number of individuals entering the ﬁshery (see also Ebert 1998). For several species, fertilisation rates have been shown to be negligible when individuals are more than 4 m apart (Pennington 1985, Levitan et al. 1992, Styan 1997). Sea urchins are often aggregated, particularly Strongylocentrotus species (Breen & Mann 1976, Himmelman 1984, Scheibling et al. 1999). Notwithstanding patchy and clumped distributions, these behaviours do not increase the number of individuals, as smaller numbers of patches and reduced patch size can continue to limit fertilisation success (Levitan et al. 1992). In California, the mean density of S. franciscanus (including sexually immature animals) in 1991 403

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was only 0.7 m−2 (S.E. = 0.07). In contrast, densities at two unﬁshed sites averaged 4.7 m−2 (S.E. = 0.30) and 4.3 m−2 (S.E. = 0.36) (Kalvass & Hendrix 1997). Interpreting these differences is difﬁcult, however, because the aggregation structure of the population is important and this attribute is not well described by transect or other area-based methods. It may be assumed that populations of sea urchins are more vulnerable to collapse at very low densities but deﬁnition of a critical density for fertilisation success will be confounded by oceanographic conditions, the extent to which aggregations of sea urchin are labile and so on. Early studies of fertilisation efﬁciency were based only on a few experiments and assumed a threshold distance beyond which fertilisation success was very low (Pennington 1985, Levitan et al. 1992). Lundquist (2000) modelled spawning efﬁciency to describe the effects of density, aggregation and characteristics of gamete dispersion (including hydrodynamic conditions). Studies with this model indicated varying shapes of the larval-production v. adult-stock relationship, but this relationship had the commonly assumed sharp threshold in larval production, only under very speciﬁc conditions. Under virtually all natural conditions, the relationship was a gradual change in slope, rather than a sharp threshold. These results were applied to red sea urchins (S. franciscanus) to determine the effect of ﬁshing on larval production (Lundquist 2000). The primary effect was a reduction in density itself, and depletion of high densities at speciﬁc locations. Aggregation at low densities compensated somewhat for this loss, but the Allee effect itself contributed only a small part to the decline.

Metapopulations and scales of management Most species of exploited sea urchins have relatively long larval lives (in the range of 14– 40 days; Lawrence 2001) so there is considerable potential for mixing among sub-populations within a ﬁshery. The degree to which that potential is realised will depend on, among other things, the prevailing oceanographic conditions (Ebert et al. 1994, Wing et al. 1998, Botsford 2001). For exploited species, ﬁshing overlies this complexity in population structure. Fleets do not harvest uniformly across the ﬁshery and the interaction between metapopulation structure and harvesting may have unpredictable results. There are only a few instances in which variability in spatial patterns in larval settlement have been analysed (Morgan et al. 2000a). These have been used to analyse the interactions between ﬁshing and metapopulation behaviour in the context of marine reserves (Botsford et al. 1999, Morgan et al. 1999). Although the existence of metapopulation effects has been widely canvassed in the literature, translating this knowledge into improved management is difﬁcult. The critical pieces of information needed for management advice are almost always missing: the degree of connectedness among sub-populations, the source and destinations of larvae, and the dynamics of the ﬂeet (Botsford et al. 1999, Cooper & Mangel 1999, Wilen et al. 2001). Sea urchin ﬁsheries are often managed at regional scales (100s to 1000s of km). A consequence of this scale of management is that the ﬁshing ﬂeet is able to move within the management unit to maximise catch rates. Small-scale management (arbitrarily deﬁned as less than 10 km) has been practised only in Japan, Mexico and South Korea, as well as parts of ﬁsheries in Chile and Nova Scotia. For sea urchin ﬁsheries to maximise both their harvest and the prospects of long-term sustainability they have to move from large-scale capture ﬁsheries to some form of more intensive management. Early explorations of spatial management approaches indicated their potential promise, but they depended on usually unknown 404

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parameter values. Quinn et al. (1993) demonstrated that permanent closures within a ﬁshery could provide a hedge against over-ﬁshing in the face of increasing effort but the required reserve spacing depended on dispersal distances which were unknown. Botsford et al. (1993) considered rotational ﬁshing to be valuable in populations with a threshold in abundance below which egg production declined dramatically (Botsford et al. 1993), but the dynamics of that effect were unknown.

Recruitment variability Large and unpredictable variations in recruitment seem a feature of many species of edible sea urchin (see Lawrence 2001 for reviews, see also Guillou & Michel 1993). In particular, the relationships between populations size and settlement/recruitment, and subsequent percapita survival and growth are unclear. Recruitment is as well understood for S. franciscanus as for any sea urchin. The pattern of sea urchin settlement on the north coast of California since 1990 appears to be dominated by several atypical oceanographic events in 1992–93 (Ebert et al. 1994). Recruitment appears to be episodic in northern California, Oregon, Washington and British Columbia (Bernard & Miller 1973, Pearse & Hines 1987, Sloan et al. 1987, Ebert et al. 1994, Wing et al. 1995) but less so in southern California and Mexico (Tegner & Dayton 1981, Ebert et al. 1994, see Tegner 2001 for review). In northern California, episodes of recruitment have been linked to variations in the strength of upwelling on daily timescales and associated cross- and along-shelf transport of larvae (Morgan et al. 2000a). In El Niño years, upwelling is reduced and nearshore water is not advected across the shelf to the same extent. As a result, larvae are either not transported offshore (Roughgarden et al. 1991, Ebert et al. 1994, Wing et al. 1995, Schroeter et al. 1996) or larval settlement processes change in other more complex ways (Lundquist et al. 2000, Botsford 2001). These processes, plus unexplained variation in recruitment among years and places may make S. franciscanus vulnerable to recruitment overﬁshing (Tegner & Dayton 1977, Botsford et al. 1993, Pﬁster & Bradbury 1996, Lai & Bradbury 1998). Recruitment of S. nudus along the southern coast of Hokkaido is signiﬁcantly correlated with average water temperature in September of the previous year (Agatsuma et al. 1998). High water temperature in September is thought to increase the abundance of juveniles by shortening the larval period (Agatsuma et al. 1998). Decreased water temperature further affects the ﬁshery by promoting growth of the large brown algae Laminaria religiosa and Eisenia bicyclis in deeper water (Taniguchi 1991, Agatsuma et al. 1994) which in turn promotes faster growth of Strongylocentrotus nudus (Agatsuma et al. 1994, Agatsuma & Kawai 1997, Sano et al. 1998). In the northeast Gulf of Maine, consistently poor recruitment of S. droebachiensis contrasts with the high recruitment usually recorded in the exposed southwestern region (Harris & Chester 1996, Balch et al. 1998, Harris et al. 2001). These patterns are likely to be the product of a range of processes acting on spawning success (Wahle & Peckham 1999, Seward et al. 2000), dispersal, settlement, and mortality of small juveniles and have led some to claim that a sustainable ﬁshery will not be possible in the northeast unless recruitment is augmented by stock enhancement (Harris 2000, Harris et al. 2001). In addition to these geographic differences, recruitment has declined through time in the southwest; following patterns in catch and, in 1999, recruitment was at its lowest level since 1983 (Harris et al. 2001). 405

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In Ireland, the population of Paracentrotus lividus in Lough Hyne, County Cork, has been monitored intermittently since the early 1960s and has been unﬁshed since 1981. Sea urchins were extremely dense in Lough Hyne in the late 1920s and early 1930s (Renouf 1931). Little qualitative and no quantitative information was collected over the next two decades, but by 1955, densities had declined (Muntz et al. 1965). In 1964, qualitative surveys were implemented, to be abandoned in 1966 because of an explosive increase in the abundance of sea urchins. Densities declined again in the late 1960s and the surveys were re-established in 1971 (Kitching & Thain 1983, Kitching 1987). Since the mid-1980s the population has remained at levels as low as has been measured to date (Barnes et al. 1999). The 40+ years of observations show that densities of P. lividus in Lough Hyne have varied by four orders of magnitude, mostly in the absence of ﬁshing. It is clear from these examples that improved understanding of the hydrographic basis of larval supply, settlement and growth can improve ﬁsheries management. Such an approach has been advocated in southern Chile – populations that enjoy some protection from ﬁshing because of their exposure to the southern Paciﬁc Ocean may act as source populations for those in more sheltered waters (Moreno et al. 1987, Clément et al. 1988, Castilla 2000). Large recruitment events have been observed, for example in March–April 1991 in the Mehuin Marine Reserve (Gebauer 1992), as well as other parts of Chile (e.g. González et al. 1987, Guisado & Castilla 1987, Moreno et al. 1987, Stotz et al. 1992, Zuleta & Moreno 1994). Although there are many hypotheses concerning the processes that determine settlement and subsequent survival (Zuleta & Moreno 1994), experimental tests are few and it is likely that the relative importance of these processes will vary along the 4000 km coastline of Chile. Larval development takes 39 to 48 days depending on temperature (González et al. 1987, Guisado & Castilla 1987, Bustos et al. 1991).

Minimum and maximum legal sizes Minimum legal sizes are in force in all the major ﬁsheries with the exception of those in Alaska, South Korea and New Zealand and smaller ﬁsheries, including Iceland, Ireland, the Philippines, Spain and Tasmania. Size limits have a long history in ﬁsheries management and are traditionally imposed to allow individuals to spawn once or several times before entering the ﬁshery. This conservation objective is complicated in sea urchin ﬁsheries because, unlike in many ﬁsheries, the intermediate-sized individuals are the most valuable. The poor compliance observed in several ﬁsheries, most notably in Chile, in part reﬂects this conﬂict. Given the allometric relationship between test diameter and gonad volume (as a proxy for reproductive output), there would seem a good case for greater use of MxLSs in sea urchin ﬁsheries, particularly for ﬁsheries targeting Strongylocentrotus franciscanus (see below). Maximum size limits have been used only in Maine, Washington and British Columbia (for S. franciscanus). They were abandoned in British Columbia after a relatively short period. In all instances the MxLS was used in conjunction with a MLS and in Washington attracted compliance problems. Morgan et al. (1999, 2000b) have shown that yield per recruit is close to its maximum in northern California but may beneﬁt from an increase in the MLS; however, egg production per recruit is down to 20% of unﬁshed levels. Tegner & Dayton (1977) noted that the abundance of juvenile red urchins (S. franciscanus) was highest underneath the test or spine canopies of adults. They suggested that the key issue of harvesting on subsequent recruitment is the number of canopy-providing adults 406

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remaining. Kato & Schroeter (1985) subsequently recommended a MxLS as a management tool in the red urchin ﬁshery. Based on population growth models using Washington and Oregon data, Ebert (1998) concluded that survival of large red urchins is more important for population growth than survival of small red urchins. Based on population modelling in Washington, Lai & Bradbury (1998) concluded that a MxLS reduces variability of yield and the risk of stock collapse for red urchins. Nevertheless, reliance solely on a MxLS would be risky without considerable control over exploitation rates.

Ecosystem Management The term “Ecosystem Management” has been used to describe the broadening of ﬁsheries management from a single-species focus to consideration of ecosystem effects (see Larkin 1996, National Fisheries Research and Development Institute (NFRDI) 2000 for recent compilations). Although the term has arguably escaped robust deﬁnition, the essence of it is to manage human interactions with ecosystems to preserve ecosystem integrity (itself in need of robust deﬁnition). Implicit in “managing” the effects of ﬁshing is an understanding of the processes that regulate populations, which is missing for all but a small number of species and ﬁsheries. Sea urchins are possibly one of the best candidates for multispecies or ecosystem management: they play clear and often dominant roles in the ecology of rocky reefs, their ﬁsheries are typically in shallow water and may be observed directly and manipulated, they interact with other high-value species such as abalone and lobsters, and declines in ﬁsheries provide a clear imperative for change. Nevertheless, with the exception of those in Japan and South Korea, the world’s major sea urchin ﬁsheries are managed on a single-species basis. In those countries, sea urchin ﬁsheries are managed as part of a suite of ﬁsheries (including those for crustaceans, abalone, kelp, and ﬁshes) and habitat manipulation is used extensively to increase production. In North America, only in Nova Scotia is ecological information used to develop harvest policy (Miller & Nolan 2000). The reasons that sea urchin ﬁsheries are managed mostly without reference to the ecology of reefs are probably as complex and diverse as the social and political institutions in which they are embedded. In many western countries, systems of governance and ﬁshing rights may impede approaches to management that require more than the yield of a single species to be considered. Sea otters bring the complexities of ecosystem management into sharp relief; the evidence for strong ecological interactions is relatively clear, but there are conﬂicting objectives between ﬁsheries-based legislation, which focus on sustainable yield, and marine mammal conservation, which seeks to protect and rebuild sea otter populations (Gerber et al. 1999). Attempts to balance the objectives of shellﬁsh ﬁsheries and the protection of sea otters in California by restricting the latter to ﬁxed zones have not been successful (Gerber et al. 1999). In Nova Scotia, sea urchins are most dense in a band that marks the boundary between inshore kelp forests and areas of barrens habitat in deeper water (DFO 2000b, Miller & Nolan 2000). Sea urchins in this band actively graze kelp and their roe are larger and of better quality than those from barrens habitat in the deeper water (see Scheibling 2000 for review). Individual ﬁshers are given exclusive access rights to stretches of coast and are required to harvest sea urchins so that the boundary between habitats remains stable. Management of this ﬁshery is based on the depth at which these bands are found (an index of exploitation rate) and the length of the band along the shore (an index of the size of the resource; see ﬁshery 407

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summary for more details). Although this form of management represents a signiﬁcant improvement, it makes little reference to another ecological process – disease. If populations of sea urchins in Nova Scotia collapse every decade or so because of disease then management ﬁnely tuned to sea urchin – algal interactions alone seems to miss a key process. In many jurisdictions, the governing Acts or Laws require ﬁsheries managers to minimise the ecological effects of ﬁshing and manage ﬁsheries with more than single-species objectives. In the United States, sea urchin ﬁsheries are managed by the relevant State agencies and in all states with signiﬁcant ﬁsheries (Maine, Washington, Oregon and California), there are provisions for ecological sustainability. Beyond 3 nautical miles, in the U.S. Federal government’s jurisdiction, the Sustainable Fisheries Act 1996 seeks a more ecosystem-based approach to ﬁsheries management but operational deﬁnitions for these concepts remain elusive (Fluharty 2000). Progress in implementing management plans that embody the ecological provisions of the Act (e.g. “Essential Fish Habitat’) has been slow. In Canada, the 1997 Oceans Act encourages the development of management plans that include marine environmental quality guidelines and criteria designed to protect ecosystem health. Development of these criteria is in the preliminary stages and, with the exception of Nova Scotia, management of sea urchin ﬁsheries in North America remains embedded in classical singlespecies methods. The laws governing wild ﬁsheries in Chile, Japan, the Philippines and South Korea (which together accounted for a little over two-thirds of world production in 1998) make no reference to ecosystem effects of ﬁshing or ecosystem management.

Conclusions Sea urchin ﬁsheries have a poor record of sustainability. Fisheries have declined in Japan, Maine, California, South Korea, and Washington, as well as several smaller ﬁsheries such as those in Ireland, France and the Philippines. The causes of these declines are likely to be manifold and, in the absence of stock assessments, difﬁcult to isolate. In those ﬁsheries that have been assessed, only the most simple stock assessment methods have been applied, except in Washington where more complex stock assessments have been overtaken by access and property right issues as well as budget constraints. Management is ad hoc and/or ineffective in many sea urchin ﬁsheries. The general pattern in these ﬁsheries is of depletion of different areas within the ﬁshery, often including areas of barrens habitat that contain sea urchins with poor roe recoveries. In the absence of assessments it is difﬁcult to determine whether they are over-ﬁshed or whether the declines simply represent the “ﬁsh down” of accumulated biomass. Nevertheless, the magnitude of the observed declines suggests that many of those not assessed have been over-ﬁshed and are in decline. An exception to the overall pattern of short-term “boom and bust” is the much longer-term decline in some Japanese ﬁsheries, despite enormous enhancement programmes, closures, MLSs and a management regime that provides exclusive access rights to ﬁshing co-operatives. Sea urchin ﬁsheries present all the familiar challenges for researchers and managers of sedentary stocks (Jamieson & Campbell 1998) and failure to meet those challenges are as apparent as in other taxa. Strong and persistent spatial structure in stocks paired with largescale or ineffective management and excessive effort from mobile ﬂeets all contribute to declining ﬁsheries and therefore world production. This is particularly the case for the world’s largest sea urchin ﬁshery, in Chile. In addition to these attributes, sea urchins 408

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present further complexities for management because the harvested roe serves both for reproduction and as a nutrient store, and the size and quality of the gonad varies both as a function of the reproductive cycle and the availability and type of food. As a consequence, the value of the roe is only partially known at the time of harvest. These interacting processes introduce small-scale heterogeneity over and above that noted in other taxa. Ecologically, sea urchins are often the dominant herbivore on shallow reefs and their removal can change communities enormously. Furthermore, changes in the environment over decades or longer may have enormous impacts on the recruitment of sea urchins and indirectly through the outbreak of disease or freshwater runoff. Such episodic events may have profound policy implications; deﬁning an ecologically sustainable, precautionary harvest becomes problematic in the face of large-scale, rapid changes in abundance and community composition. The biological attributes of sea urchins and the dynamics of their ﬁsheries suggest that, as for so many sedentary invertebrates (Botsford et al. 1997, Orensanz & Jamieson 1998), the greatest prospect for long-term sustainability lies in small-scale management. In addition, some form of exclusivity of access will promote enhancement and intelligent harvesting to maximise roe value. Management institutions that are capable of quickly responding to large changes in abundance, seemingly independent of ﬁshing, will provide the best hedge against uncertainty.

Dedication We dedicate this review to the memory of Dr Mia J. Tegner in recognition of her enduring contribution to our understanding of the ecology of sea urchins and their ﬁsheries.

Acknowledgements We are indebted to Sophie Brosset, Amanda Leland, Marchal Malay, John Schaefer and Andrew Sharman for help in assembling information and particularly Dr Paul Breen for discussion and criticism of the manuscript. This review was partially funded by grants (to NLA) from Te Ohu Kai Moana and the New Zealand Ministry of Fisheries.

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Wilen, J. E., Smith, M., Botsford, L. W. & Lockwood, D. R. 2001. Avoiding surprises: incorporating ﬁshermen behaviour into management models. Bulletin of Marine Science. in press. Wing, S. R., Botsford, L. W. & Quinn, J. F. 1998. The impact of coastal circulation on the spatial distribution of invertebrate recruitment, with implications for management. In Proceedings of the North Paciﬁc symposium on invertebrate stock assessment and management, G. S. Jamieson & A. Campbell (eds). Canadian Special Publication of Fisheries and Aquatic Sciences, 285–294. Wing, S. R., Largier, J. L., Botsford, L. W. & Quinn, J. F. 1995. Settlement and transport of benthic invertebrates in an intermittent upwelling region. Limnology and Oceanography 40, 316–329. Witman, J. D. & Sebens, K. P. 1992. Regional variation in ﬁsh predation intensity: a historical perspective in the Gulf of Maine. Oecologia 90, 305–315. Woodby, D. 1991. The red sea urchin test ﬁshery of southern Sitka Sound, 1990. Alaska Department of Fish and Game, Regional Information Report, 1J91–03. Juneau. Woodby, D. 1998. Adaptive cluster sampling: efﬁciency, ﬁxed sample sizes, and an application to red sea urchins (Strongylocentrotus franciscanus) in southeast Alaska. In Proceedings of the North Paciﬁc symposium on invertebrate stock assessment and management, G. S. Jamieson & A. Campbell (eds). Canadian Special Publication of Fisheries and Aquatic Sciences 125, 15–20. Woodby, D., Larson, R. & Rumble, J. 2000. Decline of the Alaska abalone ﬁshery and prospects for rebuilding the stock. Canadian Special Publication of Fisheries and Aquatic Sciences 130, 25–31. Woodby, D. A., Kruse, G. H. & Larson, R. H. 1993. A conservative application of a surplus production model to the sea cucumber ﬁshery in Southeast Alaska. In Proceedings of the International Symposium on Management Strategies for Exploited Fish Populations, G. Kruse et al. (eds). University of Alaska, Fairbanks: Alaska Sea Grant College Program Report No. 93–02; 191–202. Yano, K., Akeda, S., Satoh, J., Matsuyama, K., Agatsuma, Y. & Nakata, A. 1994. Inﬂuence of current and water temperature on the feeding behavior of sea urchins. In Techno-ocean 94. International Symposium Proceedings 1, Kobe: Japan Shipbuilding Industry Foundation, 195–198. (In Japanese with English abstract) Yoo, S. K. 2000. Mariculture. Busan, South Korea: Kooduk Press. Yoo, S. K., Hur, S. B. & Ryu, H. Y. 1982. Growth and spawning of the sea urchin, Anthocidaris crassispina. Bulletin of the Korean Fisheries Society 15, 345–358. Zamora, S. & Stotz, W. 1992. Ciclo reproductivo de Loxechinus albus (Molina 1782) (Echinodermata: Echinoidea) en Punta Lagunillas, IV Región, Coquimbo, Chile. Revista Chilena de Historia Natural 65, 121–133. Zegers, J., Oliva, M., Hidalgo, C. & Rodriguéz, L. 1983. Crecimiento de Loxechinus albus (Molina 1782) (Echinodermata: Echinoidea) en sistema de jaulas suspendidas a media agua. Memoirs of the Latinamerican Aquaculture Society 5, 369–378. Zuleta, A. & Moreno, C. A. 1994. Rubilar inves evaluación de stocks y monitoreo pesquería erizo 1990. Informe Final Convenio para Subsecretaría de Pesca y la Universidad Austral de Chile, BIP 20031748.

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TEMPORAL AND SPATIAL LARGE-SCALE EFFECTS OF EUTROPHICATION AND OXYGEN DEFICIENC Y ON BENTHIC FAUNA IN SCANDINAVIAN AND BALTIC WATERS – A REVIEW KARIN KARLSON, 1 RUTGER ROSENBERG 1 & ERIK BONSDORFF 2 1 Department of Marine Ecology, Göteborg University, Kristineberg Marine Research Station, 450 34 Fiskebäckskil, SWEDEN e-mail: [email protected], [email protected] (corresponding author) 2 Environmental and Marine Biology, Åbo Akademi University, Akademigatan 1, 20 500 Åbo, FINLAND e-mail: erik.bonsdorff@abo.ﬁ

Abstract Eutrophication has been an increasing ecological threat during the past 50 yr in many Scandinavian and Baltic marine waters. Large sedimentary areas are seasonally, or more or less permanently, affected by hypoxia and/or anoxia with devastating effects on the benthic macrofauna in, for example, the Baltic Sea, the Belt Seas and Öresund between Denmark and Sweden, the Kattegat and the Skagerrak coast towards the North Sea. In this review ﬁgures for the input of nitrogen and phosphorus to different sea areas are presented, and in several cases also changes of nitrogen and phosphorus concentrations in the water. The nutrient input is related to production levels, and related to macrobenthic infauna. Changes of dominant benthic species, abundance and biomass are presented in relation to both changes in organic enrichment and hypoxia and/or anoxia in time and space. Since the 1950s–60s, the benthic faunal biomass has increased in the Gulf of Bothnia as a result of increased organic enrichment. In the Åland Archipelago, the number of benthic species decreased since the 1970s but abundance and biomass increased. Drifting algae at the sediment surface has also been an increasing problem. The changes were caused by increasing eutrophication. In the Finnish Archipelago Sea, large-scale eutrophication has resulted in periodic bottom water hypoxia and drifting algal mats with negative effects on benthic fauna. In the Gulf of Finland, the benthic fauna has been negatively affected by hypoxic bottom water below 70 m depth since the 1960s, but with a period of improved oxygen conditions during 1987–94. In the Baltic Proper, large sea-bed areas of 70 000–100 000 km2 below 70–80 m water depth have been more or less hypoxic and/or anoxic since the 1960s with no or reduced sediment-dwelling fauna. This process was a result of increased eutrophication and lack of larger inﬂows of oxygenated water from the Kattegat. Several coastal areas and larger basins in the southern Baltic (e.g. the Bornholm Basin, the Arkona Basin and the Kiel Bay), have, on occasions, been similarly negatively affected by hypoxic bottom water. Many sedimentary areas below ∼17 m in the Danish Belt Seas have been affected by seasonal hypoxia since the 1970s with negative consequences for the bottom fauna. On the Danish Kattegat coast, the benthic fauna in the Limfjord, the Mariager fjord and the Roskilde fjord have been particularly negatively affected. In the southeast, open Kattegat, increased input of nutrients in combination with stratiﬁcation

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have resulted in seasonal hypoxia since 1980 with negative effects on benthic animals and commercial ﬁsh species in most years. Several fjords on the Swedish and Norwegian Skagerrak coast have shown negative temporal trends in bottom water oxygen concentrations, and some of them lack benthic fauna in the deeper parts for several months or more. In this review the temporal development of bottom water hypoxia and/or anoxia is discussed and consequent possible losses of sediment-dwelling faunal biomass are roughly calculated. In total for the areas investigated, the worst years of hypoxia and/or anoxia combined may have reduced the benthic macrofaunal biomass by 3 million t. This loss is partly compensated by the biomass increase that has occurred in well-ﬂushed organically enriched coastal areas. Tolerance of some Baltic species to hypoxia and/or anoxia is discussed and also their different strategies to cope with hypoxia and/or anoxia and H2S.

Introduction Eutrophication in Scandinavian waters Excessive nutrient enrichment has been an increasing problem in Scandinavian waters for several decades (i.e. in the Baltic Sea, the Öresund and Belt areas between Denmark and Sweden, the Kattegat and the Skagerrak coast (Fig. 1)). Human activities have drastically increased the load of nutrients since the 1950s and increases may be as high as 4-fold for total N and about 8-fold for total phosphorus (Larsson et al. 1985, Rosenberg et al. 1990). Decreased transparency, changes in macroalgal distribution, increased amounts of drifting algal mats, harmful algal blooms and extension of laminated sediments (varved layers, undisturbed by bioturbation) (Persson & Jonsson 2000) can be mentioned as examples of ecological changes in these marine systems (Jonsson et al. 1990, Norkko & Bonsdorff 1996, Kautsky 1999, Larsson & Andersson 1999). Today about 85 million people live in the drainage area of the Baltic Sea. They put a large anthropogenic pressure on the environment, and the resulting eutrophication is one of the most important threats to the marine ecosystem (HELCOM 2001). The increased eutrophication has, as a secondary effect, led to increased oxygen consumption on the sea bed. As a consequence, areas with hypoxia and anoxia have extended, especially at deep areas below the halocline (Unverzagt 2001). More or less enclosed areas, like the Baltic Sea in Scandinavia and the Chesapeake Bay on the US east coast, are dependent on a renewal of the bottom water to maintain an oxygen concentration suitable for benthic life. Such inﬂows of bottom water may, however, be irregular and vary in magnitude, which makes some enclosed areas especially vulnerable to oxygen deﬁciency. In the Baltic Sea, oxygen saturation has been measured in several basins over the last 100 yr. The oxygen concentrations in the bottom water show a more or less continuous decline from about 3 ml l−1 in the beginning of the twentieth century to <1 ml l−1 in the mid1950s (Fonselius 1969) (Fig. 2). Some measurements <2 ml l−1 were recorded around 1930. Already in the 1960s, Fonselius (1969) could predict what was going to happen in the Baltic Sea, and he wrote (p. 50): If the development continues in the Baltic deep water, the whole water mass below the halocline will soon turn into a lifeless “oceanic desert” such as is found in the Black 428

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Sea. The present stagnation will probably lead to a catastrophe for the bottom fauna in the deep areas of the whole Baltic. At the time of Fonselius’ pioneering work, the Baltic Sea entered a new phase with continuing hypoxia or anoxia in the deep waters that still exists today. For instance, the distribution 429

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of laminated sediments is estimated to cover one-third of the total sedimentary area of the Baltic Proper, which represents a 3.5-fold increase since the 1960s (Jonsson et al. 1990). In waters that are affected by eutrophication-induced hypoxia (here deﬁned as O2 concentrations <2 ml l−1), seasonally or irregularly, a reduced benthic faunal community may be found, often characterised by opportunistic species (Pearson & Rosenberg 1978). The Kattegat has since the beginning of the 1980s been affected by seasonal hypoxia, which has been suggested to be correlated with the over threefold increase in N input during 1960 and 1970 (Rosenberg et al. 1990). The existence of anoxia and hypoxia is, consequently, often a combination of water stratiﬁcation and stagnation, and increased oxygen consumption at the bottoms caused by eutrophication.

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Macrobenthic infauna in Scandinavian waters The Baltic Sea has a short geological and ecological history. It was formed after the latest glaciation about 15 000 yr BP. The salinity of the Baltic Sea has since then altered between fresh water and saline water and the present condition with brackish water was formed about 2000 BP (Westman et al. 1999). Due to its narrow outlet and shallow sills, and the considerable river input of fresh water, the Baltic Sea may be classiﬁed as a large estuary. Because the Baltic Sea is a relatively young brackish water ecosystem, and because the environmental conditions are extreme with steep environmental gradients (low salinity, variation in temperature, water stagnation, etc.) (Leppäkoski & Bonsdorff 1989, Kautsky & Kautsky 2000), relatively few species have adapted to the Baltic conditions. Some infaunal species are of fresh water origin (for example Monoporeia afﬁnis and Saduria entomon) and some are saltwater species adapted to lower salinity (for example Nereis (= Hediste) diversicolor, Pontoporeia femorata and Macoma balthica). Currently, new additions to the fauna (e.g. Marenzelleria viridis) also play an important role in the benthic system (Olenin & Leppäkoski 1999, Leppäkoski & Olenin 2000). Thus, the salinity gradient, which characterises the waters around Scandinavia, is an important factor structuring the faunal communities. Off the Swedish west coast the fauna is diverse both with respect to species richness and function. The Bothnian Bay, on the other hand, is characterised by a low diversity of two to three species (Rumohr et al. 1996, Bonsdorff & Pearson 1999, Laine 1999). Further information about the Baltic ecosystem and the benthic fauna can be found in Elmgren (1984) and Laine et al. (1997), and on successional stages and functional groups of the benthos in Rumohr et al. (1996), Bonsdorff & Pearson (1999), and Rosenberg (2001). Considering the different faunal functions, different groups dominate in different areas. For example, mobile sediment surface detritivores (Monoporeia afﬁnis, Mysis relicta) dominate in the inner Bothnian Bay and are also common in the Bothnian Sea. These species are positioned relatively close to the sediment surface and have relatively short mean (1–2 cm) and maximum penetration depth (3–5 cm) (Rumohr et al. 1996). A comparatively higher diversity is found in the Baltic Proper, with dominance of mobile sediment surface detritivores and/or carnivores and/or semimobile detritivores and/or carnivores (for example Monoporeia afﬁnis, Pontoporeia femorata, Saduria entomon, Macoma balthica and Harmothoe sarsi). The principal absence in this area of the “conveyer belt” organisms, which actively transport organic and inorganic particles between the sediment interface and the lower levels, limits the bioturbated zone in general to the upper 2 cm of the sediment (Rumohr et al. 1996). However, the recently introduced polychaete species Marenzelleria viridis is deepburrowing, and has spread rapidly in the Baltic Sea. M. viridis builds L-shaped burrows extending down to a depth of about 35 cm (Zettler 1997). In the Kattegat and the Skagerrak, diversity is higher and the organisms are generally larger. The benthic fauna is frequently found close to the sediment surface but can penetrate several centimetres, either by building tubes (e.g. Terebellides stroemi, Trochochaeta multisetosa) or by actively burrowing (e.g. Calocaris macandreae and Nereis diversicolor) and have thus an important role in increasing the depth of the bioturbating layer (Rosenberg et al. 2000). In the Kattegat, many soft bottom sediment-dwelling infaunal communities are characterised by Amphiura ﬁliformis, a suspension feeding brittle star. Echinoderms are strictly marine and therefore, in general, absent in the Baltic Sea.

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Aims and scope In this review temporal and spatial extensions of hypoxia and/or anoxia in Scandinavian waters since the 1950s are presented and the consequences for benthic macrofauna are discussed. Different sub-areas are described beginning in the northern part of the Baltic Sea, the Gulf of Bothnia, continuing south down to the Baltic Proper, including the Gulf of Finland and the Gulf of Riga, and south to the Belt Sea, Kattegat and Skagerrak (Fig. 1). Furthermore, the ecological consequences of the faunal losses in terms of “missing faunal biomass” are discussed, together with consequences for the ﬁsh stock, loss of bioturbating activity and consequences for biochemical processes. In addition, hypoxia and/or anoxia as a structuring factor, involving physiological adaptations among species to cope with oxygen depletion and hydrogen sulphide are discussed.

Effects of hypoxia/anoxia on macrobenthic infauna Gulf of Bothnia The Gulf of Bothnia (Fig. 1) in this study includes the Bothnian Sea in the south and the Bothnian Bay in the north. Maximum depths of the Bothnian Sea and the Bothnian Bay are 230 m and 147 m, and the average depths are 68 m and 43 m, respectively. A shallow sill of 20 m, the Northern Quark, separates the two basins. The salinity of the Bothnian Sea is about 5 at the surface and 6–7 in the bottom water, and of the Bothnian Bay about 1–3 at the surface and 4 at the bottom. The saline water, which enters from the surface of the Baltic Proper, is diluted by the freshwater inﬂow from numerous rivers. Generally there is a decreasing diversity of the macrofauna from the Bothnian Sea in the south (about ﬁve species) to the Bothnian Bay in the north (about two species), but with a possible increase in the most northern part of the Bothnian Bay because of contributions from freshwater fauna (Elmgren et al. 1984). Also the biomass and abundance decrease northwards. The biomass decreases on average from about 100 g m−2 in the south, to on average 1 g m−2 in the north, and the abundance from a mean of several 1000 m−2 in the south to a few 100 m−2 in the Bothnian Bay. The decrease of fauna with longitude can partly be explained by the shorter productivity season in the northern part. However, the northward decrease in salinity progressively excludes many species. In general, the macrofauna in the Bothnian Sea shows greater similarities to that of the northern Baltic Proper than to the fauna in the Bothnian Bay (Elmgren et al. 1984). The oxygen concentration is about 6–8 ml l−1 in the Bothnian Sea and about 7.5–9.5 ml l−1 in the Bothnian Bay (UMF 1999). Long stagnation periods are prevented due to weak stratiﬁcation and vertical mixing of the water masses. As a consequence of spring warming and autumn cooling, the upper 60 m water layer turns over twice a year. High oxygen concentrations can therefore be maintained in the bottom water layer. No signiﬁcant longterm trends are found in oxygen concentration of the near bottom water layer in the Bothnian Sea or in the Bothnian Bay (HELCOM 1996, UMF 1999), and according to HELCOM (1996), anoxia has never been observed in the open sea. However, UMF (1994) has reported

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a decreasing trend since 1960 in oxygen concentration with 0.13 ml l−1 yr−1 (from about 6.5 ml l−1 to about 5 ml l−1) in the bottom water layer at one station (175 m) in the southern Bothnian Sea. Oxygen consumption rates in the deep water (below 175 m) have been calculated to be 8 ml m−2 month−1 in the Bothnian Sea and 2 ml m−2 month−1 in the Bothnian Bay, which indicates a lower load of organic matter in the Bothnian Bay (UMF 1996). Sedimentation rates of organic carbon in the open water areas are found to be lower compared with the coastal zones. In open water areas of the Bothnian Bay the sedimentation rate is calculated to be about 1.0 mmol carbon m−2 day−1 and in the Bothnian Sea to be about 2.5 mmol carbon m−2 day−1, which can be compared with 10 mmol carbon m−2 day−1 at a coastal station in the northern Bothnian Sea (UMF 1996). It has been suggested that agriculture is the largest source of nutrients of anthropogenic origin transported to the sea by river water (HELCOM 2001, Kauppila & Bäck 2001). Fluctuations of the river nutrient load are mainly due to variation in precipitation. Another source is air deposition. Nutrients are also transported between the different basins of the Baltic Sea. Total N concentrations have increased in the whole Gulf of Bothnia, both in the surface water and close to the sea ﬂoor, with an increase of about 1% yr−1 during the period 1970–93 (HELCOM 1996). Total P also shows an increasing trend of 1% yr−1 in the surface water during the same time period. The increased nutrient concentration might be an indication that the Gulf of Bothnia, to some degree, may also be affected by the general nutrient enrichment found in most of the Baltic Sea. In 1974, Macoma balthica and Mytilus edulis were dominant at depths between 0 m and 25 m in the Bothnian Sea. These species were entirely absent in samples from the Bothnian Bay, where generally no ﬁlter feeders exist. In deeper strata of the Bothnian Sea, Monoporeia (= Pontoporeia) afﬁnis and Saduria entomon were totally dominant, whereas in the Bothnia Bay, Monoporeia afﬁnis was dominant in both depth strata. Halicryptus spinulosus was also found in the shallow stratum of the Bothnian Sea. A similar species composition was found between 1989 and 1993 (HELCOM 1996). However, the newly introduced polychaete species Marenzelleria viridis has increased both in abundance (>5 times) and number of locations between 1995–97. The most rapid increase has been in the northern Bothnian Sea, and it seems to primary colonize coastal zones (UMF 1998). The absence or reduction of marine species such as Harmothoe sarsi and Pontoporeia femorata can possibly be explained by the general long-term decrease in salinity in the Baltic Sea caused by the long stagnation period since the1950s (HELCOM 1996). A comparison of the benthic fauna in 1974 was made with earlier investigations, one in the 1920s and two in the 1960s in the Gulf of Bothnia (Elmgren et al. 1984). A good agreement was found between all periods concerning the number of species. When comparing biomass, an agreement was found between the faunal composition in the 1960s and 1974 (Elmgren et al. 1984). However, the biomass in the 1920s was distinctly lower (almost four times, weighted mean of the whole depth stratum) in the Bothnian Sea (including the Åland Sea) compared with the later investigations. The increased biomass could, according to the authors, possibly be explained by differences in methods, or indicate a gradual general eutrophication of the Bothnian Sea. In a study by Cederwall & Blomqvist (1991) (depth not given), signiﬁcantly increased biomasses were found in 1983–87 in the Bothnian Sea, but not in the Bothnian Bay, compared with stations sampled in the 1920s and in the 1950s. Saduria entomon, Macoma balthica

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and Monoporeia afﬁnis, which together made up >95% of the biomass, showed signiﬁcantly increased biomasses (on average > ﬁve times) compared with the 1920s. Further, Cederwall & Blomqvist (1991) suggested that the biomass increase has mainly occurred since the 1950s, and that eutrophication was the principal cause. According to UMF (1994), the macrofaunal biomass has increased since the 1960s by four times in the Bothnian Sea. The increase was suggested to possibly be because of large-scale eutrophication. Finnish scientists demonstrated ﬂuctuations in the biomass of the numerically dominant benthic fauna in the Bay of Bothnia during 1965–83 (Andersin 1986). A signiﬁcant increasing trend was noted at most stations, which was mainly caused by the high biomass values in the 1980s. The biomasses around 1980 were about 100–200% higher compared with 1965 (up to 7.4 g afdw m−2). No drastic change in the bottom water oxygen concentration was observed. According to HELCOM (1996), signiﬁcant long-term increasing trends of both total biomass and total abundance have occurred since the 1960s in the Bay of Bothnia. The increase was most pronounced in the Bothnian Sea. Eutrophication was suggested to be the most likely cause. Since the mid-1980s, the increase of the fauna has levelled off in the two basins. Because of local water discharges, deterioration of the fauna has been found in coastal areas. Some examples follow below. Two areas, Rauma (2.5–16 m depth) and Uusikaupunki (3–25 m depth) (Fig. 1) located along the southwest coast of Finland in the southern Bothnian Sea, were studied to investigate long term changes in the benthic fauna between 1973–90 and 1963–90, respectively (Mattila 1993). Both areas are affected by municipal and industrial wastewater. Changes in the bottom fauna in both study areas reﬂected changes in the pollution/eutrophication level in the respective areas. Sediments near the discharge areas were nearly or totally defaunated and were depleted of oxygen (concentration not given) in the late 1960s and early 1970s. However, these areas have slowly recovered because of improved wastewater treatment. In 1973, 40% of the stations investigated were listed as polluted or very polluted compared with only 11% in 1986. On the contrary, according to Mattila (1993), sediment-dwelling fauna communities near the open sea area showed signs of deterioration during the 1980s, which was explained with respect to the general eutrophication in the Baltic Sea. Generally, there was a dominance of Macoma balthica both in total biomass (>90–95%) and total densities (>60–70%) and a strong decrease of Monoporeia afﬁnis. In the Rauma area, M. afﬁnis almost disappeared between 1986 (10 000 ind. m−2) and 1990 (44–78 ind. m−2), and in the Uusikaupunki area a change in species composition, from M. afﬁnis- to Macoma balthica-dominance, could be observed at many stations between the 1970s and 1980s. Later changes in the species composition in the Uusikaupunki area were, for example, a disappearance of Saduria entomon and Harmothoe sarsi, while taxa such as Oligochaeta and Nereis diversicolor increased and occurred in larger areas. The effect of a sulphate pulp mill on the benthic macrofauna in a ﬁrth of the Bothnian Sea was studied by Rosenberg et al. (1975). No fauna was found in an area of 1 km2 and total biomass was estimated to be reduced by about 60 t. The species found to be most tolerant to pollution was Macoma balthica, while Monoporeia afﬁnis, Harmothoe sarsi and Halicryptus spinulosus were missing in the polluted area. The effect on the fauna showed similarities with another polluted area outside the town Turku (Fig. 1) in southwestern Finland (Tulkki 1960, Leppäkoski 1975).

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The soft-bottom macrofauna (7–54 m depth) was investigated outside the town Sundsvall (Fig. 1), located in the western Bothnian Sea, between 1985 and 1996 (Svelab 1996). According to this investigation, in 1996 the investigated areas with no or with one or two species constituted 81% of the area, an increase of about 23% compared with the period 1985–91. In 1996, Monoporeia afﬁnis had the smallest extension since 1985 and had decreased by about 35%. Even though the total load of nutrients had decreased in the area since 1980 (total N by about 40% and total P by almost three times), several of the stations investigated showed decreased oxygen concentrations in 1996 compared with the period 1980 –90. In summary, an increase in macrofaunal biomass has occurred in the Gulf of Bothnia since the 1950s–60s, which was most pronounced in the Bothnian Sea and peaked in the mid 1980s. However, no major change in species composition seems to have occurred. It has been suggested that the cause for the biomass increase was related to eutrophication. Because of good vertical mixing of the water masses, oxygen concentrations have remained high. Locally, however, oxygen deﬁciencies have been registered, with accompanying negative effects on the fauna.

The Åland Archipelago and the Finnish Archipelago Sea The Åland archipelago The Åland archipelago consists of 6500 islands and has a shoreline of several thousand kilometres (Fig. 3a). The area has mostly shallow sea beds, <30 m deep. The southwestern part has deeper regions, down to 290 m. The water ﬂow is from south to north in the eastern part (water from the Gulf of Finland and the Archipelago Sea) and from north to south in the western part (water from the Bothnian Sea). The salinity is between 5 and 7. The largest source for P and N loading is ﬁsh farming. The loading has decreased between 1987 and 1998. Other important source of P and N loading is agriculture. In 1998, the annual local load of nutrients was estimated to be nearly 60 t of P and 600 t of N, which is about 50% less than in 1989 (Nummelin 2000). In the Åland archipelago the winter concentrations of P and N increased signiﬁcantly between 1983 and 1994 in the productive (1–10 m) layer. P values almost doubled, from about 12 to 21 µg l−1, while N increased from about 240 to 290 µg l−1. This led to a decreased N : P-ratio. During the same time period the Secchi depth has decreased from about 8 to 4 meters, and also bottom water oxygen concentrations showed a decreasing trend, although no hypoxia has been registered in this exposed area (Bonsdorff et al. 1997a). The summer nutrient values showed a more stable trend during 1984 and 1999 (Nummelin 2000). The inner archipelago areas typically had a high organic content in the sediment and reduced oxygen saturation in the near-bottom water. Seasonal hypoxia is common in these areas (Fig. 3a). The oxygen conditions improved towards the open coastal areas, and organic content of the sediment decreased (Bonsdorff et al. 1996). Defaunated or almost dead sediments were found mainly in the deeper areas in the inner archipelago and near ﬁsh farms (Östman & Blomqvist 1997). Zoobenthic data are present for the Åland archipelago from the beginning of 1970 (Helminen 1974, 1975). Comparisons of data from the 1970s and data collected towards the

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Figure 3 Baltic Archipelago areas with hypoxia (<2 ml l−1) and anoxia are indicated in black (irregular shading indicates uncertain areas: a) Åland Archipelago, 1989–1996, ∼70 km2, (based on information from Jumppanen & Mattila 1994, Bonsdorff et al. 1996, Östman & Blomqvist 1997), b) Stockholm Archipelago, hypoxic areas ∼60 km2 (based on information from Lännergren & Eriksson 2000, Persson, pers. com.), c) the Himmerfjärd and Askö areas (based on information from Elmgren & Larsson 1997). Areas with laminated sediment indicating oxygen deﬁciency in the bottom water: d) St Anna Archipelago, ∼25 km2 in the 1990s (based on information from Persson & Jonsson 2000).

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end of the 1980s and beginning of the 1990s show that the number of species had decreased signiﬁcantly, while abundance and biomass increased signiﬁcantly in areas ≤30 m deep (Bonsdorff et al. 1990, Bonsdorff et al. 1991, Norkko & Bonsdorff 1994). This temporal change in zoobenthic communities is typical of areas affected by increasing organic enrichment (Pearson & Rosenberg 1978, Rumohr et al. 1996). Of four sub-areas studied, the largest changes had occurred in the inner archipelago areas (Färjsundet–Lumparn), while the more exposed areas in the southwest were less affected. All areas, however, showed clear effects of eutrophication since the 1970s (Bonsdorff et al. 1990). A principal component analysis revealed that oxygen is the key parameter for number of species, whereas N and organic content of the sediment seem to affect the biomass (Bonsdorff et al. 1990). In deeper areas (20–40 m) the innermost, sheltered stations followed this same trend, while the outermost, exposed stations were in better condition, even if an increase in both abundance and biomass was revealed (Norkko & Bonsdorff 1994). In these areas a signiﬁcant correlation could be found between the organic content of the sediment and the density of zoobenthos (Norkko & Bonsdorff 1994). Besides the increased abundance and biomass, the zoobenthic communities also showed an altered community composition between the 1970s and the 1990s. The increased biomass was mainly because of increased abundance of Macoma balthica. A shift in relative importance of species was evident for stress-tolerant taxa, such as chironomid larvae and M. balthica, which were more abundant in the 1990s, while the abundance of Monoporeia afﬁnis had decreased (Bonsdorff et al. 1991). In the 1990s the benthic communities were dominated by Macoma balthica and Chironomidae in the inner archipelago zone, whereas M. balthica and Monoporeia afﬁnis were dominant in the middle archipelago. The distribution of M. afﬁnis had moved further towards the open sea areas, where they were often found to be the dominant species (Bonsdorff et al. 1990, 1991, 1996). About 40% of the species composition had changed since the 1970s, and a shift from suspension feeders to deposit feeders had occurred, indicating functional disturbances as well (Bonsdorff & Blomqvist 1993, Bonsdorff et al. 1997a,b). A reinvestigation of the entire area in the year 2000 revealed a decrease in abundance and biomass, corresponding to the reduction in nutrient loading and benthic hypoxia (Nummelin 2000, Perus et al. 2001). The species composition had not changed, however, indicating that the previously noted functional shifts have persisted in this system. In shallow and intermediate depths eutrophication has also caused another ecological effect, the presence of drifting algal mats. They are mainly a problem for the outer, exposed archipelago areas, which have not frequently been affected by hypoxia or anoxia. These mats, with a biomass of up to 2000 g ww m−2, may cover several hectares of the shallow bottoms and they may induce rapid oxygen depletion with serious effects for the zoobenthic communities (Bonsdorff 1992, Norkko 1997, Berglund 1998). This aspect is further reviewed in Norkko et al. (2000). According to Vahteri et al. (2000), this may be a more widespread and general problem than previously anticipated, with large-scale consequences for the entire archipelago ecosystem (Norkko et al. 2000).

The SW Finnish Archipelago The SW Finnish Archipelago (Fig. 1) is one of the largest archipelagos in the world. It has an area of 8300 km2 and has an enormous topographic complexity. It consists of about 25 000 islands and has a shoreline of >12 000 km. The mean depth is only 23 m, and the 437

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maximum depth is 146 m. Several rivers discharge into the area bringing approximately 190 t of P and 2300 t of N to the area annually, corresponding to about 70% for P and 45% for N of the total loading. Since the 1980s, ﬁsh farming in the middle and outer archipelago areas has contributed to higher nutrient levels, accounting for ca 10% and 4% of total loading of P and N, respectively. However, during summer months the P loading from ﬁsh farms may account for more than 50% of the total P loading. Salinity varies between 5.5 and 6.5. The water stratiﬁes mainly due to differences in temperature in summertime. The ﬂow is mainly in the direction from the Gulf of Finland and the northern Baltic Proper through the Archipelago to the Bothnian Sea (Jumppanen & Mattila 1994, Bonsdorff et al. 1997a,b, Kirkkala 1998, Kauppila & Bäck 2001). In the Archipelago Sea the overall nutrient levels are high, indicating large-scale eutrophication. In the inner archipelago the concentrations have decreased, while the opposite occurred in the outer archipelago between 1965 and 1993 (Bonsdorff et al. 1997a). From 1981–82 to 1993–95, the P levels have increased from 15–17 µg l−1 to over 20 µg l−1 in the inner areas. In the outer areas the increase has been more signiﬁcant, from less than 14 µg l−1 to over 20 µg l−1 in most areas. Levels <14 µg l−1 were found only in the outermost exposed areas. The N levels have not increased as much and were between 300 µg l−1 and over 400 µg l−1 in the inner areas, and between 200 µg l−1 and 400 µg l−1 in the middle and outer areas (Kirkkala 1998). From 1965 to 1993 a signiﬁcant decrease in water transparency has been noticed; pelagic primary production and the amount of annual ﬁlamentous algae have increased (Jumppanen & Mattila 1994, Bonsdorff et al. 1997a). As a direct consequence of the decreased nutrient loading from municipal wastewater treatment plants, a decreasing trend in nutrient levels in the inner archipelago waters of the Archipelago Sea has been observed, and oxygen values in the near-bottom water have increased signiﬁcantly since 1970 (Jumppanen & Mattila 1994, Bonsdorff et al. 1997a,b). In the central and outer parts the trend is the opposite as the nutrient concentrations have increased and the oxygen reservoir has decreased between 1970 and 1993 (both early and late summer values) (Jumppanen & Mattila 1994, Bonsdorff et al. 1997a,b). Zoobenthos has been studied in the inner parts of the Archipelago Sea since 1950 (Sjöblom 1955, Tulkki 1964, Leppäkoski 1972, 1975). In these early studies the main objectives were to investigate the effects of local pollution on the zoobenthic communities, and the studies were concentrated on the innermost archipelago areas. The zoobenthic communities improved off major cities between the 1950s and 1986 because of improved wastewater treatment. Since then, however, the development has been the reverse. Monoporeia afﬁnis has disappeared from many areas, and the previous Macoma-Monoporeia communities have been replaced by Macoma-Nereis (today also Marenzelleria) communities (Mattila 1993, Jumppanen & Mattila 1994). In the middle and outer parts of the Archipelago, zoobenthos has been studied since 1960 (Tulkki 1960, Bagge et al. 1965), but new studies, with which those early data could be compared, are relatively scarce (Jumppanen & Mattila 1994, Hänninen & Vuorinen 2001). The analysis of Hänninen & Vuorinen (2001) clearly illustrates the effects of increased organic loading, registered as an increased abundance of Macoma balthica and Monoporeia afﬁnis. Fish farming is probably the reason that these nutrient concentrations in the middle and outer archipelagos have increased, and subsequent changes are reﬂected in the zoobenthos. Kraufvelin et al. (2001) studied the recovery potential of the zoobenthos at two localities in the Archipelago after pollution abatement from ﬁsh farms, and found extensive differences in the recovery potential because of differences in

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topography and water exchange patterns between the localities. The Finnish Archipelago is also affected by drifting algal mats, which occur down to 45 m depth (Vahteri et al. 2000). The entire benthic ecosystem of this area must be considered at risk because of both drifting algal mats and periodic hypoxia and anoxia (Vahteri et al. 2000, Kauppila & Bäck 2001).

Gulf of Finland The Gulf of Finland (Fig. 1) is in direct connection with the central Baltic Sea. The depth is decreasing towards the east, being approximately 100 m in the mouth area, about 60 –80 m in the middle and 20– 40 m in the most eastern part. It has a drainage area of 421 000 km2 and an annual river inﬂow between 100 km3 and 125 km3, which is about one-quarter of the total freshwater input to the whole Baltic Sea (HELCOM 1996, 2001, Alenius et al. 1998). Salinity gradients are formed due to the freshwater inﬂow, mainly from the river Neva in the east, and the inﬂow of saline (8) deep water from the Baltic Proper (Alenius et al. 1998). At the opening a halocline is found at 60–80 m. Towards the east, the difference between surface and bottom water salinities decreases. The salinity has, however, wide seasonal variations (HELCOM 1996, Alenius et al. 1998). A decrease in salinity (from approximately 7) in the near-bottom water layer has been noted during the period 1965–95 (Perttilä et al. 1995, HELCOM 1996). The decrease in salinity is in agreement with other areas in the Baltic Sea, and is because of the long stagnation period, caused by the lack of major inﬂows of saline water from the Kattegat since the 1950s. Between the mid 1960s and mid-1970s low oxygen concentrations (3–5 ml l−1) were recorded in the Gulf of Finland, and H2S could be detected far into the Gulf at a depth of 65 m (Andersin et al. 1978, Andersin & Sandler 1991, Pitkänen & Välipakka 1997) (Fig. 4a,b). Later, in the middle of the 1980s, the distinct halocline weakened, because of decreased salinity, which made improved mixing of the water masses possible. As a consequence, oxygen concentrations increased in the bottom water layer (7–9 ml l−1) between the late 1980s and early 1990s (Pitkänen & Välipakka 1997). Since the middle of the 1980s, at one station (60–80 m depth) in the middle of the Gulf and another towards the east (58–62 m depth), oxygen concentrations have been >2 ml l−1 (HELCOM 1996, Laine et al. 1997). With increasing coastal eutrophication, local hypoxia or anoxia in the sediments have been documented along the entire Finnish south coast (Kauppila & Bäck 2001). The Gulf of Finland has been considered to be one of the most eutrophic areas in the Baltic Sea. The largest loads of nutrient and organic matter are discharged from the River Neva and from St Petersburg and its surroundings with about six million inhabitants. It has been estimated that the Gulf receives annually 140 000 t of N of which 70% enters the eastern part (Pitkänen et al. 1997, Gran & Pitkänen 1999). A clear increase in nutrient concentration from the west towards the east is found, and the phytoplankton biomasses are about two to ﬁve times greater than in the open sea (Leppänen et al. 1997). Nutrient measurements have shown an about 2-fold increase in N concentrations during the 1970s and 1980s, while in the beginning of the 1990s the concentrations seemed to have stabilised (HELCOM 1996). Phosphorus, on the other hand, has shown a decreasing trend in the bottom water layer since the 1980s (HELCOM 1996, Leppänen et al. 1997), which is, at least in part, because of improved oxygen conditions since the mid-1980s (Perttilä et al.

439

a)

KARIN KARLSON, RUTGER ROSENB E R G & E R I K B O N S D O R F F

FINLAND

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Figure 4 The Gulf of Finland, bottom areas with hypoxia (<2 ml l−1, dashed) and anoxia (shaded): a) the best (630 km2) and b) the worst situation (5700 km 2) between 1963 and 1989 (based on information from Andersin & Sandler 1991), estimated between Russarö and Odensholm lighthouse, c) oxygen concentration <2.1 ml l−1 in the eastern Gulf, 300 km2 in 1996 (based on information from Pitkänen & Välipakka 1997). For the Finnish coastal waters anoxic sediments are in addition locally recorded along the entire south coast (Kauppila & Bäck 2001).

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1995, Lehtoranta 1998). There are indications that the sediments of the eastern Gulf of Finland act as an efﬁcient trap for P and N (Ignatieva 1997). Because of signiﬁcant landbased inputs of Mn and Fe to the Gulf, large portions of P are probably absorbed on metal oxides in oxic sediments (Ignatieva 1997). In general, the benthic infauna was sparse in the Gulf of Finland from the mid-1960s until the late 1970s, with the exception of the central part of the Gulf (58–65 m) where relatively high abundances (up to 2300 ind. m−2) were found between 1965 and 1967, dominated by Pontoporeia femorata and Macoma balthica (Andersin et al. 1978, Andersin & Sandler 1991, Laine et al. 1997). Otherwise, at depths around and below 70 m, the sediments were devoid of fauna or had sparse communities dominated by M. balthica or Harmothoe sarsi (<100 ind. m−2). The development of the benthic assemblages during the 1960s along the southern (Estonian) coast of the Gulf of Finland was described in detail by Järvekülg (1979). Between 1987 and 1994, a considerable improvement of the bottom fauna took place associated with increased oxygen concentrations (Andersin & Sandler 1991, Seire 1991, Laine et al. 1997). Between 60–80 m depth in the central- and western part of the Gulf, total abundance could reach 3500–4800 ind. m−2 and total biomass was 57–70 g m−2. The benthic communities were dominated by Pontoporeia femorata (62–94%) and Monoporeia afﬁnis (25–37%). Also, Harmothoe sarsi, Macoma balthica and Saduria entomon were found, but generally in lower numbers (Laine et al. 1997). Below 90 m, however, abundance and biomass decreased rapidly (in general <100 ind. m−2 and <1 g m−2) (Seire 1991). In the eastern part of the Gulf (26.5°E–27.5°E) similar abundances and biomasses to those recorded in the central and western Gulf were found around 70 m in 1989–90, with dominance of Pontoporeia femorata (Seire 1991). However, investigations in the innermost Gulf of Finland, in the Neva Estuary (<25 m), showed a signiﬁcant change in benthic faunal composition between 1982 and 1996 because of anthropogenic stress (Telesh et al. 1999). In 1982–84, freshwater bivalves (Pisidiidae) constituted about 75% of the total biomass, whereas in 1996 other freshwater animals (Oligochaeta, Unionidae and Chironomidae) dominated, and a 10-fold increase of the biomass could be found in some areas. In autumn 1996, extensive areas of the bottom sediments in the open eastern Gulf of Finland suffered from hypoxia or anoxia (<1.0 ml l−1 in September 1996 at a depth of 45 m) (Pitkänen & Välipakka 1997). The area of anoxic sediments was calculated to be 3000 km2 in the whole eastern Gulf based on a critical depth of 40 m (Fig. 4c). In the semi-enclosed coastal basins, hypoxia or anoxia were found at depths below 25–30 m (Alimov et al. 1997). The oxygen concentrations were also lowered in the western Gulf, but anoxia was not detected there. Such extensive areas of low oxygen concentration have not been detected since 1966 in the open eastern Gulf. As a consequence, in 1996–97, a sudden collapse of the fauna occurred (Laine & Andersin 1998). The low oxygen concentrations were explained by an exceptionally strong density stratiﬁcation caused by the warm and calm late summer conditions and a large inﬂow of saline water in 1993–94 from the Kattegat into the Baltic. In addition, the eutrophication in the area may intensify the events of hypoxia through increased degradation of organic matter and therefore accelerated oxygen consumption (Leppänen et al. 1997, Pitkänen & Välipakka 1997). Because of the anoxic conditions, large amounts of P were released from the sediment to the water. The load was estimated to correspond to about one year of anthropogenic load of P to the Gulf (4500 t) and the water concentration of P increased up to 200 mg m−3 (generally the deep water concentrations varied between 30 mg m−3 and 60 mg m−3) (Pitkänen & 441

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Välipakka 1997). Further, as a result of increased benthic release of P, the inorganic N : P ratio of the deep water decreased from 5 : 1 to 2 : 1, and later this caused heavy blue–green algal blooms during the summers of 1996 and 1997 (Leppänen et al. 1997, Pitkänen & Välipakka 1997). These blue–green algae blooms were the greatest since the 1980s (Alimov et al. 1997). Gran & Pitkänen (1999) have calculated the denitriﬁcation rates in sediments in the eastern Gulf, which varied between 0.1 and 17.6 mg N m−2 day−1 (giving on average 5 g N m−2 yr−1). The highest rates were found in areas with high abundance of bioturbating fauna (up to 3500– 4700 ind. m−2) dominated by Monoporeia afﬁnis and Pontoporeia femorata. The result shows that the bioturbating fauna is of major importance for the denitriﬁcation process. Further, denitriﬁcation is an important process to counteract eutrophication. In conclusion, the increased abundance of the fauna since the mid-1980s was probably primarily because of the improved oxygen conditions caused by enhanced vertical mixing. Later, the serve hypoxia in 1996–97, which caused a collapse of the fauna, was presumably caused by decreased vertical mixing of the water masses in combination with eutrophication.

Gulf of Riga The Gulf of Riga (Fig. 1) is relatively isolated from the rest of the Baltic Sea, mainly because of the two islands Saaremaa and Muhu. The average depth of the Gulf is about 27 m with a maximum depth of 62 m. Exchange of the water occurs through the two sounds; the Irbe Sound located in the west and the Muhu Sound in the north. Average salinity in the Gulf can vary greatly, from 0.5–2 in surface layers in spring at the southern district, to 7.5– 7.7 in spring and summer in the water layers at Irbe Sound (Berzinsh 1995). The drainage basin of the Gulf has been estimated to cover more than seven times the surface area of the Gulf (Ojaveer 1995). Consequently, the Gulf is strongly inﬂuenced by river transport including considerable loads of nutrients, heavy metals and other pollutants. Also, the city of Riga is polluting the Gulf (Andrushaitis et al. 1995). The ﬁrst signs of deterioration were noted in the 1950s (Ojaveer 1995). During the 1980s there was an approximately 50% increase of P and the N concentration had approximately doubled (Andrushaitis et al. 1995, HELCOM 1996). According to Laznik et al. (1999), the Gulf has received an average of 113 300 t of N and 2050 t of P annually during the time period 1977–95. Large inter-annual variations were attributed mainly to natural variations in water discharge (18–56 km3 yr−1). Even though the Gulf of Riga has been considered to be one of the most polluted areas in the Baltic Sea (Andrushaitis et al. 1995), the loads of nutrients were rather low or moderate compared with those reported for many other drainage areas of the Baltic Sea (Laznik et al. 1999, Stålnacke et al. 1999). A 50% decrease of N loads has been reported by HELCOM (1996) for the Gulf of Riga between 1987 and 1993. However, according to Laznik et al. (1999) there is no evidence of a downward trend during 1990 to 1995. Long-term investigations (1963–90) have shown a steady decline in the oxygen concentration, and the decrease rate grows from the surface towards the bottom (Berzinsh 1995, Ojaveer et al. 1999). In August, the average concentration has decreased from approximately 6.3 ml l−l to about 4.5 ml l−1 between 30 m and 50 m depth. Minimum concentrations could be as low as 1.8–3.0 ml l−1; in extreme cases anoxic conditions occurred. The decline was explained by increased oxygen consumption caused by increased oxidation of organic mat-

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ter. Between 1960 and 1980, the average oxygen consumption rate was calculated to have increased by approximately 30% (Berzinsh 1995). However, according to HELCOM (1996) the negative trend between 1964 and 1993 in oxygen concentration in the near-bottom layer measured in August, was explained by stratiﬁcation, and as a consequence limited vertical mixing. The subsequent tendency for oxygen concentration increase after 1993, was, accordingly, explained by improved vertical mixing. Changes of the soft-bottom fauna have been described since the end of 1950 in the Gulf of Riga. In a review by Cederwall et al. (1999) the soft-bottom fauna in 1993–96 have been compared with earlier studies. A short summary of their study is as follows. During the 1970s, compared with the period 1958–63, a 4-fold increase of total abundance was found, with an increase of all major groups, especially worms and crustaceans. During the 1980s, the total abundance had declined almost to a half caused among other things, by a reduction of worms, mainly oligochaetes. During the 1990s, on the other hand, the most important changes were the decrease in numbers of crustaceans (40%) and an increase in the number of worms (Marenzelleria viridis) (almost 4-fold). Biomass measurements, however, showed a successive increase (5- to 6-fold) between the period 1950 to 1980, mainly attributable to Macoma balthica but also to Marenzelleria viridis. The increased biomass might reﬂect the increased nutrient loading during the same time period (Andrushaitis et al. 1995, Cederwall et al. 1999). During the 1990s, the total benthic biomass decreased by 45%. Production estimations indicated a shift from higher production during the 1980s to a lower level during the 1990s. The shift was mainly caused by a marked decrease of Monoporeia afﬁnis (by 50%), which dominated during the 1980s, and an increase in dominance (abundance and biomass) of Marenzelleria viridis in the 1990s. The decrease of Monoporeia afﬁnis was explained by the constant decrease in oxygen concentrations until 1993. Kotta & Kotta (1995) studied the macrobenthos of the Pärnu Bay (5–10 m) (northern Gulf of Riga) in 1991, and compared the faunal composition at that time with that in 1959– 60 (Järvekülg 1979). They reported an approximately one and a half to eight times increase in biomass, and the regions with high biomass and abundance had spread from the central part of the bay towards the Pärnu River in the north. The northern part of the bay was contaminated with toxic substances and enriched in nutrients from the Pärnu River and the town of Pärnu. The majority of oligosaprobic species (Piscicola geometra, Idotea viridis, Jaera albifrons and Leptocheirus pilosus) had disappeared; only the mesosaprobic Macoma balthica had been able to expand its distribution. The non-indigenous species Marenzelleria viridis was ﬁrst found in the Gulf in 1988 and since then the population has grown and spread to become the most dominant species during the 1990s (11.8 g ww m−2, which corresponds to 13% of the biomass). The ecological importance of M. viridis in the Gulf is not clear. The growth and production of this species have been studied in the southern part of the Gulf of Riga by Cederwall & Jermakovs (1999). They indicated that M. viridis might be a better competitor for food than Monoporeia afﬁnis as they are both deposit feeders. In summary, observed modiﬁcations of the benthic faunal communities seem to have been correlated with changes in oxygen concentrations. However, the observed increase in biomass during 1950 to 1980 was most likely an effect of eutrophication. Measured changes in oxygen concentration were probably caused by a combination of factors. Improved vertical mixing of the water-masses increased oxygen concentration, while decreased oxygen concentrations may be an effect of eutrophication.

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Baltic proper Open water areas The deep basins have, during the last 100 yr, had irregular periods of stagnation causing hypoxia and therefore, temporarily, have been devoid of benthic macrofauna. However, since the early 1950s, the oxygen concentrations in the bottom waters have declined. A prolonged period of stagnation followed the great water inﬂow in 1951–52. Signiﬁcant inﬂows has been observed later, but none of them has been large enough to raise the oxygen concentration to the same level as the inﬂow in 1951–52 (Falandysz et al. 2000). In addition, the increased eutrophication since the 1950s has, as a secondary effect, led to increased oxygen consumption above the sea bed. Laine et al. (1997) concluded that the oxygen consumption in the deep waters is higher today than at the beginning of the century. They also indicated that the oxygen conditions suitable for macrobenthic communities even after re-oxygenation of the deep waters will be less optimal during periods of stagnation. Consequently, areas with hypoxia and anoxia and deterioration of fauna have extended at depths below the pycnocline (semi-permanently at 70–80 m) since the middle of this century (Fig. 5). Above the pycnocline, on the other hand, biomass and production of benthic fauna have increased as an effect of the eutrophication (Cederwall & Elmgren 1980, 1990). The load of nutrients to the Baltic Sea has drastically increased over the last c. 100 yr because of human activities. According to Larsson et al. (1985), the increase may be about 4-fold for total N and about 8-fold for total P. Since measurements of nutrients began (N from the beginning of 1970 and P since the end of 1950), a continuous increase of both N and P concentration in the water has been recorded. In the Gotland Basin, the winter surface concentrations (0–10 m) of both N (from 2.44 ± 0.40 µmol l−1 (average ± SD) ) and P (from 0.27 ± 0.09 µmol l−1) have been at least doubled (HELCOM 1996). The most important increases of both nutrients occurred between 1969 and 1978. In contrast with phosphate, nitrate continued to increase until 1983 and thereafter both nutrients have ﬂuctuated around high levels (HELCOM 2001). During the previous decade, a continuous decrease in total P has been detected (Larsson & Andersson 1999). The annual average concentration in the surface layer (0–15 m) has been reduced by about 20% (from 0.7 to 0.55 µmol l−1). It is not possible, however, to establish whether the observed reductions are mainly an effect of reduced P input or by causes not directly inﬂuenced by man, for example, climatic changes (Larsson & Andersson 1999). The total N concentrations have, after an increase in the concentration at the mid 1990s (annual average concentration 23 µmol l−1), again decreased to the level similar to that measured in the beginning of the 1990s (annual average concentration 20 µmol l−1) (Larsson & Andersson 1999). Comparing the different sub-basins in the Baltic Proper, no major differences in nutrient concentrations have been noted with the exception of the Western Gotland Basin, where higher P concentrations indicate up-welling of nutrient rich water (Larsson & Andersson 1999). Concurrently with the nutrient increase, an increased primary production has been reported. Elmgren (1989) concluded that there has been a 30–70% increase in primary production since the turn of the century, which, according to him, corresponded to a 70–190% increase in deposition of organic carbon on the bottoms. Similarly, Jonsson & Carman (1994) reported a more than 1.7-fold increase in organic matter (loss of ignition, LOI) in the northern Baltic Proper sediments since the 1920s when comparing their data with sediment 444

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Figure 5 The Baltic Proper, recent bottom areas with temporary (∼45 000 km2, dashed) ) and almost permanent hypoxia (<2 ml l−1) and anoxia (∼40 000 km2, shaded). (Based on information from SMHI homepage: www.smhi.se/, Swedish Environmental Protection Argency homepage: www.environ.se).

data from Gripenberg (1934) and Hasselrot (1971). An increase in primary production, followed by an increased sedimentation, would be the most likely explanation for the increase in organic matter. Jonsson & Carman (1994) estimated that carbon originating from primary production and external sources has increased 5- to 10-fold during the 1980s using calculations based on annual sequestering of carbon in laminated sediments. Jonsson et al. (1990) described a large-scale distribution of laminated sediments. They estimated this area to be one-third of the total sedimentary area of the Baltic Proper, which is a 3.5-times increase since the 1960s. In the deepest part of the Baltic Proper, laminated sediments have been formed periodically during the post-glacial period. However, the lamination has expanded, especially during the last 50 yr because of the increase of sediments 445

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with hypoxia and anoxia and, as consequence, the loss of burrowing macrofauna. The “missing” macrofaunal biomass due to anoxia, has been estimated to be up to 1.7 million t ww (Rosenberg 1980). According to Jonsson et al. (1990), the expansion of the lamination will probably now be halted since it has reached the level of the pycnocline. However, in later investigations, laminated sediments have also been found in shallow waters, for example, in the inner Stockholm archipelago (Rosenberg & Diaz 1993) and in the St Anna archipelago (Persson & Jonsson 2000). In summary, extensive bottom areas in the open Baltic Sea are affected by hypoxia and anoxia. It is suggested that this is a consequence of increased eutrophication in combination with a low frequency and reductions in magnitude of oxygenated water transported into the Baltic Sea during the last 50 yr.

The eastern Gotland Basin and the northern Baltic Proper Hydrographic data from the 1950s indicate that the bottom water oxygen concentration was rather low at this time in the northern Baltic Proper. Measurements below 1 ml−1 could be reported <150 m (Fonselius 1969, Andersin et al. 1978). However, the benthic fauna was fairly diverse and dense even below 100 m at the beginning of the 1950s (Sjöblom 1955, Andersin et al. 1978). Mass occurrence of Diastylis rathkei was recorded, and other common species were Pontoporeia femorata and Scoloplos armiger. Defaunated bottom sediments were reported from <100 m depth at the end of the 1950s. By 1960, sediments devoid of fauna had increased and extended all the way into Gulf of Finland (Shurin 1968, Andersin et al. 1978, Järvekülg 1979). From 1968, H2S was recorded frequently in the deep parts (<150 m) of the northern Baltic Proper, and in 1972 defaunated bottom areas were observed even at 75 m depth. The most common species in the deep bottom communities (<100 m) from 1961 and onwards was Harmothoe sarsi (Andersin et al. 1978). During the period 1965–94, there had been no or only sporadic occurrence of macrobenthic animals <100 m in the eastern Gotland basin and northern Baltic Proper (Laine et al. 1997). None of the inﬂows during this period were strong enough to replace the anoxic bottom water in the deeper areas <100 m. However, the inﬂow in 1993–94 seemed to have improved the oxygen conditions (Laine et al. 1997). In June 1994, oxygen concentrations of 3.0–3.2 ml l−1 were noted at 200 m depth, and such high concentrations had not been measured since the 1930s. Since the mid 1990s, oxygen concentrations decreased again. In 1999, the situation was the worst recorded since the 1970s. At depths <80 m, oxygen concentrations were in general <2 ml l−1, and <135 m H2S was found throughout the year (Andersson et al. 2000). Zmudzinski (1978) stated that “benthic deserts” would occupy large portions of these sub-halocline areas, strengthening the conclusions of Shurin (1968). The present ﬁndings by Laine et al. (1997) conﬁrm the pattern predicted. In the Gotland Deep (240 m) in the eastern Gotland basin, the ﬁrst observations of defaunated sediments were made in 1949–50 (Shurin 1968, Andersin et al. 1978), and at the end of 1950, H2S was recorded (Fonselius & Rattnansen 1970). As a consequence of the poor oxygen conditions, a steady decline in benthic biomass has occurred below the pycnocline since the 1950s (Andersin et al. 1978). During 1979–92, the boundary between oxic and anoxic water was between 110 m and 160 m depth (HELCOM 1996). However, in 1993 the deep water became slightly oxygenated (<1–2 ml l−1) due to the large inﬂow from the Kattegat this year. In a later investigation in November 1998, H2S was detected at depths of <125 m, and oxygen concentrations of <2 ml l−1 were found deeper than 70 m (Andersson et al. 1999). 446

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In the southernmost part of the eastern Gotland Basin (north of the Gdansk deep) at a depth of 90 m, a benthic fauna with 250 ind. m−2 was recorded around 1950 (Demel & Mankowski 1951). Scoloplos armiger and Pontoporeia feromata were numerically dominant, and Macoma balthica dominated in biomass. Later investigations during the period 1965–74 showed a continuous decrease in both biomass and abundance, and the benthic communities became dominated by polychaetes, particularly Scoloplos armiger. Crustaceans and lamellibranchs seemed to have disappeared at the end of this period. Above 100 m, recolonization of benthic macrofauna had taken place in 1963 and 1975– 76 in connection with major inﬂows of North Sea water. A long stagnation period followed, from 1977 to 1993 (Laine et al. 1997). However, a gradual recovery of the benthic community in the late 1980s and early 1990s was observed between 60 m and 100 m depth (Laine et al. 1997). According to the authors, this can be explained by a gradual weakening of the halocline as a consequence of the prolonged stagnation. A weakening of the halocline improved vertical mixing, which in turn improved the oxygen conditions at this intermediate depth. The benthic community structure between 60 m and 100 m depth in different areas of the Baltic Proper has been described by Laine et al. (1997) during three periods, 1965–75, 1977–85 and 1986–94, which correspond to the major hydrographic changes described above. In the southern and central Eastern Gotland Basin total average abundance (≈200 ind. m−2) was quite similar comparing the three periods. However, a shift in the species composition was observed from clear dominance in abundance of the polychaete Scoloplos armiger during the ﬁrst period to a community dominance by Harmothoe sarsi, Pontoporeia femorata and Macoma balthica in the period 1986–94. In the northeastern Gotland Basin and northern Baltic Proper at depths between 60 m and 100 m, there has been a large increase in total average abundance comparing the three time periods described above (8.3, 175 and 472 ind. m−2, respectively), with Harmothoe sarsi, Pontoporeia femorata and Macoma balthica as dominant species during all three periods. For the coastal slopes, a similar pattern was reported by Olenin (1997) in a broad description of the benthic communities of the Eastern Gotland Basin. In conclusion, changing oxygen conditions affecting the benthic fauna have occurred between 60 m and 100 m depth in these basins, and above the pycnocline depending on different hydrodynamic events. In the deep basins, conditions with anoxia and H2S have increased due to eutrotrophication.

The western Gotland Basin Investigations by Sjöblom (1955) and Shurin (1968) in the western Gotland Basin (Fig. 5) during the middle and the end of the 1950s, indicated that the deep sediments were devoid of fauna (Andersin et al. 1978). During the period 1968–75, low oxygen concentrations, <2 ml l−1, were reported. Later in this period, H2S could be observed annually, and sediments <80 m depth were devoid of macrofauna. In the depth interval 30 m to 70 m, biomass of the benthic fauna in the western Gotland Basin almost doubled from the late 1970s (70 g m−2) to the late 1980s (120–140 g m−2). At the beginning of the 1990s, a decrease was observed (60 g m−2). The observed changes in biomass were mainly attributable to Macoma balthica and Saduria entomon. For abundance, some general trends have been found (analysed in the depth interval 30 m to 70 m); a decrease of Harmothoe sarsi, Pygospio elegans and Mytilus edulis was noted, 447

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whereas Saduria entomon increased during the 1980s. During the 1990s, on the other hand, it appears that Monoporeia afﬁnis and Saduria entomon decreased, whereas Halicryptus spinulosus increased (Cederwall 1999). It is proposed that the increases in biomass of benthic fauna during the 1970s and 1980s were a response to the measured increase in nutrient concentration since the 1970s (Cederwall 1999) with a probably enhanced food availability for the benthos. However, the possible reasons for the following decrease in biomass at the beginning of the 1990s are unknown. Investigations of the benthic fauna at two stations at 96 m and 109 m depth, with the same latitude as the southern tip of Gotland, have shown a steady decline in both abundance and number of species since 1994, and in 1998 no animals were found at these stations. A steady decline in oxygen concentration during this time was associated with the reduction of the fauna. In 1999, the oxygen concentration was generally <2 ml l−1 at depths <70–80 m during the whole year, and H2S could be found in a layer close to the bottom (Andersson et al. 2000). In the Landsort Deep (Fig. 1) (depth about 450 m) located between the Northern Central Basin and northwestern Gotland Basin, anoxic conditions with formation of H2S were observed in 1968 (Fonselius 1969, Andersin et al. 1978). Harmothoe sarsi was observed the year before anoxia. At the end of the 1960s and the beginning of the 1970s, however, this area was devoid of fauna (Fonselius 1969, Andersin et al. 1978, HELCOM 1996). During the 1990s, large changes in the oxygen concentration have been observed in the Landsort Deep. Between 1990 and 1994 there was an increase from 0.3 ml O2 l−1 to 1.5 ml l−1, which was later followed by a decline (at the end of 1999 H2S was observed; Andersson et al. 2000). Oxygen concentrations and salinity showed opposite trends during the 1990s. The decreased oxygen concentration at the sediment surface can be explained because the inﬂow of saline water strengthened the stratiﬁcation and reduced the renewal of oxic bottom water (Andersson et al. 2000). In summary, enhanced biomass of benthic fauna above the pycnocline during the 1980s can be associated with increased nutrient concentrations, which probably was followed by an increased sedimentation of organic matter. Below the pycnocline, low oxygen concentrations (<2 ml l−1) caused depletion of the fauna. However, it is not known whether the increase of biomass above the pycnocline in this area compensates for the loss of biomass below.

The southern Baltic Proper The southern Baltic Proper here includes the Gdansk Basin, the Bornholm Basin, the Arkona Basin and the Pomeranian Bay (Fig. 5). Tulkki (1965) and Leppäkoski (1969, 1971) investigated the benthic fauna in the Bornholm Basin in 1963–64 and 1965–71, respectively. They also compared their data with earlier investigations conducted between 1951 and 1954 (Demel & Mulicki 1954). In 1951–52, Macoma calcarea and Astarte borealis dominated the standing crop and constituted 60–98% of the biomass at <70 m (Demel & Mulicki 1954). This community, described as characterising the Bornholm Basin (Hagmeier 1930), almost disappeared during the stagnation period in the 1950s that followed the great inﬂow of saline North Sea water in 1951. During this period, the oxygen concentrations in general remained <2 ml l−1 and sometimes close to zero in the Bornholm Basin at 90 m (Filarski 1959, Fonselius 1962). In 1962–68, several inﬂows of saline water occurred to this area, followed by rapid declines in oxygen concentrations in the basin below 80 m depth. Periodically H2S was also 448

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detected (Leppäkoski 1969, 1971). In 1965, Scoloplos armiger constituted 88–100% of the sediment-dwelling fauna, this being one of the most tolerant species to low oxygen concentration in the southern Baltic (Mulicki 1957), and is probably able to utilise the accumulated food after a long period of stagnation. Consequently, the communities that earlier were dominated by lamellibranchs in the Bornholm basin and also in the Gdansk Deeps, were subsequently replaced by polychaete-dominated communities (i.e. S. armiger, Harmothoe sarsi, capitellids) (Andersin et al. 1978). After the inﬂow in 1969, an extensive recolonisation of fauna occurred in the Bornholm Basin with a great number of species present, including Macoma calcarea and Astarte borealis, and immigrants that had not been found before (Leppäkoski 1971). The community was dominated by Scoloplos armiger, which constituted more than 75% of the biomass. Oxygen concentrations up to 6 ml l−1 were measured in the bottom water. The biomass reached approximately the same level in 1970 (between 10–30 g m−2 at most stations) as in 1951–52. However, later in 1970 and 1971 oxygen deﬁciency was re-established (<1 ml l−l below 80 m) with an almost complete disappearance of the fauna (Leppäkoski 1971). “Missing” biomass was estimated to be in the order of 140 000 t ww (Rosenberg 1980). The upper limit of “dead” sediments was approximately at 80 m depth, and the most tolerant species to the low oxygen concentrations were Heteromastus ﬁliformis, Scoloplos armiger and Trochochaeta multisetosa. Warzocha (1985) investing the bottom fauna community in the southern Baltic area, Bornholm Basin, southern Gotland and Gdansk Basin in the period 1978–83 and compared his data with the data of Demel & Mulicki (1954) from 1948–52. Below the pycnocline, they reported a decrease in biomass at all stations. In spite of the reported increased primary production in the southern part of the Baltic during this time (Renk et al. 1985), no statistically signiﬁcant differences in total biomass of macrofauna between the periods were detected above the pycnocline. The species composition and the biomass were similar in both periods at stations situated on the slopes between 40 m and 70 m depth. The lack of biomass increase was suggested by Warzocha (1985) to be related to the topography of the sea bed and the current system in the southern Baltic. The currents carry organic matter from the mostly sandy areas of the bottom above the pycnocline to the deep sediments where it cannot be utilised because of the scarce fauna. Improved conditions with recolonisation of fauna (ﬁrst re-appearing were Scoloplos armiger and Harmothoe sarsi) were temporary. From 1980 onwards, considerable ﬂuctuations in the sediment fauna composition, abundance and biomass have resulted from alternating periods of stagnation and temporary improvements in oxygen conditions after inﬂows of saline water. During the period 1979–93, macrofauna was only found in 1979, 1980, 1984, 1985 and 1986 in the Bornholm Basin at 90 m depth (Osowiecki 1991, Andersin et al. 1990). In 1988–89, anoxic conditions appeared below 80 m (HELCOM 1996). A decrease in number of species (from 5 to 0–3) and biomass (from maximum 6.4 g m−2 to mean 0.7 g m−2) was reported in the Bornholm Basin at 90 m depth during this period (HELCOM 1996). In 1980–93, in the southern part of the Bornholm Basin (60–70 m), the number of species reported was limited to six and the maximum biomass was 15 g m−2 (Warzocha 1995, HELCOM 1996, Osowiecki & Warzocha 1996). Dominant species were Macoma balthica, Harmothoe sarsi and Scoloplos armiger (HELCOM 1996, Osowiecki 1991, Osowiecki & Warzocha 1996). According to historical data, these sedimentary habitats harboured a rather diverse benthic fauna during the ﬁrst half of the twentieth century. Approximately 20 species could be found, which were dominated by S. armiger, Terebellides stroemi and Astarte 449

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spp. The infaunal biomass varied between 5 g m−2 and 40 g m−2 (Hagmeier 1926, 1930, Demel & Mulicki 1954). Above the pycnocline, on the other hand, an increase in biomass by about 30– 40% was reported since 1975 for the Bornholm Basin (HELCOM 1996). During the late 1990s, low oxygen concentrations were noted in the Bornholm Basin. In 1998, the limit for 2 ml l−1 oxygen concentration was at 70 m depth and below this H2S could be detected (Andersson et al. 1999). In summer 1998, the area with H2S had extended and could be found northwards to the Hanö Bay. In spite of low oxygen concentrations, Diastylis rahtkei and Astarte borealis were observed, although, in low concentrations. However, below 80 m no fauna was found (Warzocha 1995). The Gdansk Basin (Fig. 5) is under strong anthropogenic inﬂuence especially from river outﬂows. In 1972–93, the annual load of total P discharged by the Vistula river into the Gdansk Basin ranged from 4000 t in 1982 to 11 000 t in 1978. Approximately 73% of the total P load is derived from the river runoff (HELCOM 1996). During the same period the annual load of total N from the Vistula was about 55 000–165 000 t and about 93% of the total N load originated from the rivers ﬂowing into the basin (HELCOM 1996). In 1989–93, winter nitrate concentrations were the same as in the previous decade in most areas. However, the overall negative trend in oxygen concentration observed on the bottoms of the Gdansk Basin since the 1960s, has at least periodically been reversed, and no hydrogen sulphide was found from 1990 to 1994 (Trzosinska & Lysiak-Pastuszak 1996). The overall oxygen deﬁciency at 80 m depth decreased in the Gdansk Basin from 80–90% at the end of the 1970s and the beginning of the 1980s to 40–50% in the early 1990s. However, at the near-bottom water layer the increase was not so well deﬁned. The improved oxygen condition was explained by weakening of the stratiﬁcation and by subsequent improved vertical mixing in 1994 (Trzosinska & Lysiak-Pastuszak 1996). Since the mid-1960s, a reduction (in species numbers, abundance and biomass) of the fauna has occurred in the Gdansk Basin below 100 m depth. Species found here were Halicryptus spinulosus, Scoloplos armiger and adult Macoma balthica. (Osowiecki & Waszocha 1996). However, in later decades, Harmothoe sarsi was the only species found, and in 1989 and 1993 no permanent macrofauna was recorded (Osowiecki 1991, Warzocha 1995, Osowiecki & Waszocha 1996). Above 60 m in the inner part of the Gdansk Bight, on the other hand, no signiﬁcant changes in the macrofaunal community have taken place between the 1970s and 1990s, although large ﬂuctuations in both biomass and abundance were recorded (HELCOM 1996). Between 10 and 18 species were recorded in the coastal zone (30 m depth) with bivalves generally in dominance (Osowiecki & Waszocha 1996). In summary, a community shift from a dominance of lamellibranchs to a dominance of polychaetes has been observed between the 1960s and 1970s in the Gdansk Basin and the Bornholm Basin. In general, polychaetes are more tolerant as a group towards different kinds of disturbance compared with bivalves (Rumohr et al. 1996). It is suggested that the overall decrease in biomass below the pycnocline was a result of depleted oxygen conditions. Olenin (1996) classiﬁed the macrobentic fauna found in 1980 and 1992 in the Curonian Lagoon (Fig. 5) (mean depth 3.7 m) and at the coastal zone outside the lagoon (depth 20– 25 m) into functional groups. The lagoon contains mainly fresh water while the coastal zone is mesohaline. Large amounts of organic matter are deposited in the lagoon (62 000 t yr−1) (Pustelnikov 1983). The macrofaunal species occurred in a successional pattern in relation to the organic enrichment gradient according to the Pearson-Rosenberg (1978) model (Olenin 1996). In the southern stagnant area, an impoverished community was found represented by benthic deposit feeders, most often totally dominated by chironomids and oligochaetes. In 450

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the northern and the central hydrodynamically active parts, an abundant and diverse fauna was found represented by the “Dreissena polymopha” community, which forms an effective natural bioﬁlter, and the “Valvata” community, which reworks the ﬁrst few cm of the seabed sediment. The communities of the coastal zone were relatively diverse in species and functions, and were dominated by a “Macoma balthica” community. In the Pomeranian Bay (Fig. 5) during the 1980s, 10 to 13 species were found each year, with an abundance of 600–26 000 ind. m−2 and an average biomass of 379 g m−2 (depth not given) (Osowiecki 1991). Bivalves contributed up to 95% of the average biomass including the following species: Mya arenaria, Cerastoderma glaucum, Macoma balthica and Mytilus edulis. In 1993–94, Kube et al. (1996) investigated the benthic macrofauna in the Pomeranian Bay (depth <20 m) and the Arkona Basin (Fig. 5) (stations at 20–30 m depth, max. 53 m). The number of species identiﬁed was 45. Mean total biomass decreased from about 100 g afdw m−2 in the southwest of the bay, at the Oder River Mouth, to only about 10 g afdw m−2 at the Arkona Basin in the north. In the shallow parts, depth <20 m, ﬁlter feeders were dominant (the obligatory ﬁlter-feeding bivalve Mya arenaria constituted 80–90% of the total biomass, followed by the facultative ﬁlter-feeding polychaetes Marenzelleria viridis and Nereis diversicolor, whereas surface deposit feeders (including Macoma balthica) gradually increased in importance northwards to the deeper part, 20–30 m, in the Arkona Basin. Kube et al. (1996) proposed that physical disturbance and food availability were important in structuring the benthic community. Further, they suggested that the high biomass in the southwest of the bay might be because of high inorganic and organic loads from the Oder River. In late summer 1994, hypoxia (<37% saturation) and anoxia with H2S was detected in the southwestern Pomeranian Bay (Fig. 6) at 9–11 m (Powilleit & Kube 1999). The event was explained by a combination of extraordinary meteorological and hydrographical conditions together with eutrophication. An extremely hot summer resulted in thermal stratiﬁcation, which was additionally enhanced by an upwelling of saline, oxygen-depleted water from the Arkona Basin. The situation probably lasted between several days and a few weeks. At the most affected areas, species number, abundance and biomass of the benthic fauna decreased signiﬁcantly and re-colonisation was still not complete 2 yr later. The decrease in total biomass was mainly because of mass mortality of large specimens of Mytilus edulis and Mya arenaria. In the Arkona Basin (depth 44– 48 m) (Fig. 5) signiﬁcant reductions in mean numbers of species (from 16.2 to 3.2–6.8), biomass (75 to 4.4 –5.4 g m−2) and abundance (from 633– 1097 to 119–316 ind. m−2) have occurred between the periods 1979–83 and 1989–93 comparing two stations investigated (HELCOM 1996). Reduced oxygen concentrations were suggested to be the cause for the reduced fauna (concentrations not given). In 1994 –95, an increase in both abundance (up to 1500 ind. m−2) and biomass (up to 20 g ww m−2) changed the negative trend of decreasing fauna recorded during the 1980s (DMU 1998). During the 1990s, the composition of the fauna changed in the Arkona Basin, with a general increase in dominance of polychaetes and a reduction of crustaceans (DMU 1998). This was in accordance with what has been observed from long-term investigations at one station in the Öresund and one station in the Great Belt (DMU 1998). Bottom water oxygen concentrations were <1 ml l−1 in the Arkona Basin during the years 1988, 1992 and 1994, and this was probably the cause for the reduction of the fauna in the years 1989 and 1993. In conclusion, an increased importance of polychaetes was found in the Arkona Basin during the 1990s. High organic loads are the most likely cause for the repeatedly occurring oxygen depletion and the shift in community structure. 451

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Oder Bank

Oder

GERMANY 0

20 km

POLAND

Figure 6 The Pomeranian Bay, estimated bottom areas with hypoxia (<2 ml l−1, dashed, ∼390 km2) and anoxia (shaded, ∼170 km2) in the summer 1994 (based on information from Kube 1996, Powilleit & Kube 1999).

Swedish East Coast The Stockholm Archipelago The Stockholm Archipelago (Fig. 3b) has for many decades been a large receiver of wastewater from sewage plants. In addition, the outﬂow from Lake Mälaren west of Stockholm contributes a considerable amount of nutrients to the area, about 5000 t of N and 200 t of P annually (Rosenberg & Diaz 1993). The inner Stockholm Archipelago is an enclosed area with a few narrow connections with the outer archipelago and with vertical density stratiﬁcation for most of the year. The clear signs of eutrophication can be explained by the large nutrient input together with the restricted water exchange (Brattberg 1986). The bottom 452

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waters of the inner archipelago have suffered from hypoxia and anoxia at least since the 1950s (Stämfors 1978, Lännergren & Värnhed 1992). Improved sewage treatment (biological treatment and P removal) was introduced during the period 1968–73. Hereafter, the P load decreased signiﬁcantly from 3.3 t day−1 to 0.8 t day−1. An improved water quality in the inner part of the Stockholm Archipelago and a decreased volume of sulphur bacteria (Beggiatoa spp.) mats were later recorded (Brattberg 1986). The decreased P concentration and the enhanced N : P ratio were considered to be the most essential factors responsible for this improvement. Contrary to P, the input of N continued to increase gradually in spite of the improved sewage treatment and was estimated at that time to be 25 t day−1. Consequently, P replaced N as the most limiting nutrient in this area (Brattberg 1986). In autumn 1991, Rosenberg & Diaz (1993) observed sediments with soft black mud smelling of H2S between 9 m and 50 m depth in the inner archipelago. Sulphur bacteria mats (Beggiatoa spp.) were also reported at most stations and there was little evidence of surface or subsurface faunal activity. According to the authors, it was not known whether the bad oxygen conditions at the sea bed in the inner archipelago were because of bad water exchange that year or increased nutrient enrichment, or if it was only seasonal temporary conditions or an effect of increased large-scale eutrophication. However, in later investigations of the inner archipelago, benthic fauna has been found down to depths of 20–30 m in the inner parts, and down to depths of 40–60 m in the outer, with biomasses up to 65 g m−2 and 155 g m−2 in the inner and outer part, respectively (Stehn & Dromberg 1999). Bottom habitats (<5 m depth) with fauna were estimated to cover 76% and sediments without fauna were estimated to cover 14% of the area. The smell of H2S could, however, in general be detected in sediment at depths below 10–20 m. Dominant benthic species and faunal groups were Macoma balthica (many small individuals, length 2–3 mm), Saduria entomon, Potamopyrgus jenkinsii, Monoporeia afﬁnis, Oligochaeta and Chironomidae. The polychaete Marenzelleria viridis has increased markedly in the archipelago, up to 10 times, since 1996 (Stehn & Dromberg 1999). In conclusion, reduction of the P input from the sewage treatment has resulted in improved surface water quality in the inner Stockholm Archipelago. It is not known whether the bad conditions observed in 1991 were only seasonal or because of a large-scale eutrophication of these enclosed waters. In the late 1990s, improved conditions were noted. The Himmerfjärd and Askö areas (Fig. 3c) in the southern Stockholm Archipelago consist of several basins divided by shallow straits with a mean depth of 17 m. Input of nutrient comes from several sewage plants. Nutrients are also transported to the area from streams and land runoff, which vary largely with precipitation. Similar to the inner archipelago of Stockholm, the N input has increased gradually in the area between 1977 and 1985 (Elmgren & Larsson 1997). However, improved N reduction of the waste water at the sewage plants resulted in a reduced N load in 1992 to the same level as at the end of the 1970s (totally about 1000–1500 t N yr−1, about 50–60% of which comes from the sewage plants). P concentrations also decreased to the same levels as at the end of the 1980s (totally about 25–55 t P yr−1, about 24–36% of which comes from the sewage plants) (Elmgren & Larsson 1997). The biomass of the benthos was somewhat elevated in the inner part of the Himmerfjärd (depth 24– 41 m) before the sewage plant started to operate in 1972. After the opening of the sewage plant, the biomass of the macrobenthos increased rapidly in the inner basin until the animals died because of poor oxygen conditions (exact concentration not given) at the end of the 1970s (Elmgren & Larsson 1997). Later, increased water exchange because of storms 453

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improved the conditions in the sediments and recolonisation of benthic amphipods was possible. In 1985, the benthic fauna was killed again after a strong spring algal bloom, which caused poor oxygen conditions (exact concentration not given) at the bottoms after degradation of large quantities of organic matter. Again improved conditions after 1985, because of cold and windy summers, made recolonisation of Monoporeia afﬁnis possible in the inner parts of the archipelago. In the middle and outer part of the Himmerfjärd (depth 22–54 m), an increase of the biomass since the opening of the sewage plant has also been noted. In 1980, oxygen depletion (Fig. 3c) was also found to have reduced the fauna in some parts of this area. Macoma balthica dominated in biomass in most parts of the total area, while Harmothoe sarsi was the most common species in areas with poor oxygen conditions (exact concentration not given). Also in the reference area for the studies in the Himmerfjärd, Hållsfjärden, west of Trosa, a change of the benthic fauna has been noted, likely as a consequence of organic enrichment. Total benthic biomass increased signiﬁcantly during the period 1971–91, while the total abundance signiﬁcantly decreased during the same period (Cederwall 1992). Monoporeia afﬁnis and Pontoporeia femorata, species known to be sensitive to low oxygen conditions (see discussion), decreased signiﬁcantly in abundance and biomass, while Macoma balthica increased. In summary, locally the conditions have been improved in the Himmerfjärd, because of installation of efﬁcient sewage plants. However, a large-scale eutrophication likely effects the area. In the Askö Archipelago (Fig. 3c) at 40–45 m depth, a reduction of benthic faunal mean abundance (25 stations) has occurred since the beginning of the 1970s to the beginning of the 1990s (from >3000 ind. m−2 to 2000 ind. m−2), mainly because of a decrease in abundance of Monoporeia afﬁnis and Pontoporeia femorata (SMF 1994). The number of species was low during the 1990s (about four) compared with the 1970s and the main part of the 1980s (about ﬁve to eight) (Cederwall 1996). The biomass has, unlike the abundance, slowly increased since the beginning of the 1970s, because of the increasing numbers and increase in individual mean biomass of Macoma balthica. The change of the fauna can be described as a decrease of small, short-lived and highly productive species and an increase of large and long-lived species (SMF 1994). In the late 1990s, oxygen concentrations were >3 ml l−1, and species generally known to be sensitive to hypoxia (Monoporeia afﬁnis) and others (Macoma balthica, Chironomidae) increased in numbers. In 1996 Marenzelleria viridis was recorded for the ﬁrst time in this area. In conclusion, the Askö area was considered to be affected by eutrophication, as documented through reduced species richness and abundance, but increased biomass. In the St Anna Archipelago (Fig. 3d), spatial and temporal trends in distribution of laminated sediments were analysed from lamina-counting and sediment analyses (Persson & Jonsson 2000). The archipelago is located in the northwestern Baltic Proper and covers an area of approximately 300 km2. The area is scattered with thousands of small islands and has maximum depths between 14 m and 28 m (i.e. no part is deep enough for a distinct pycnocline to develop). According to Persson & Jonsson (2000), expansion of laminated sediments in the archipelago began in the 1960s. A rapid increase occurred during the 1970s–80s, reaching a maximum in the mid-1980s, and thereafter the laminated sediments decreased (Fig. 3d). More windy conditions in the early 1990s than in the late 1980s were suggested as an explanation for the decrease of laminated sediments in this archipelago (Persson & Jonsson 2000). The windy conditions may have caused a more efﬁcient mixing of the water column and thus, temporally improved oxygen conditions with re-settlement of burrowing fauna. In 454

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summary, the extension of laminated sediments during the 1970s and 1980s in the St Anna Archipelago was related to the high sedimentation rate of organic matter and low oxygen concentrations in this area. During the 1990s, improved oxygen conditions at the sediment surface occurred because of increased storm frequencies, which made recolonisation of some sediment-dwelling faunal species possible.

The islands Öland and Gotland By re-sampling stations around the islands Öland and Gotland (Fig. 5) that were sampled by Hessle in 1923 and 1924, Cederwall & Elmgren (1980) estimated that an over ﬁve times increase in average total biomass and an over seven times increase in average total abundance of macrofauna above the pycnocline had taken place at the latter sampling date in 1977. No major differences in species composition were reported. The increase was rather evenly spread over several species. Both suspension feeders (on average by 19 times) and deposit feeders (on average by eight times) had increased considerably. A dominance of bivalves could be observed during both surveys and a shift in species-dominance from Macoma balthica in the early survey to Mytilus edulis in the subsequent one. They considered this highly signiﬁcant biomass increase above the pycnocline to represent a real long-term change and suggested eutrophication to be the cause. Below the pycnocline (70 m depth) they reported a considerable decrease in biomass, and in 1976–77 one station was totally devoid of fauna. However, the decrease in biomass below 70 m was not signiﬁcant, but this was, according to the authors, probably because of the low numbers of stations sampled (ﬁve). At the deep stations H2S was present in the sediment. In conclusion, increased organic matter had resulted in an increase of the benthic faunal biomass above the pycnocline (70 m) between 1920 and 1977. Below 70 m, on the other hand, a decrease in fauna had occurred due to oxygen depletion. Benthic faunal investigations (depths between 1 m and 37 m) have been carried out since 1989 in the Kalmar Sound (Smith et al. 1998) located between the island Öland and the Swedish mainland (Fig. 5). A weak halocline is in general found in the area and the oxygen conditions at the sediment surface are generally sufﬁcient for benthic life. Oxygen concentrations can, however, vary locally between 0% and 100% saturation, because of restricted water exchange in some areas and enrichment of organic matter. The area receives nutrients and organic matter from local ﬁsh farms and via freshwater outﬂow from rivers and streams (Juhlin et al. 1996). Effects of eutrophication have been observed at an increased number of benthic stations investigated during the 1990s. An increased abundance and areal extension of Chironomidae have been reported (Juhlin et al. 1996, Lindquist et al. 1997, Smith et al. 1998). Macoma balthica generally dominated in biomass. An increased biomass (>100 g m−2) of M. balthica observed at several stations during the 1990s may indicate that the bivalves have been favoured by organic enrichment in areas with good water exchange. The polychaete, Marenzelleria viridis, has been found in this area since the 1990s with an abundance of 200–400 ind. m−2. Thus, the Kalmar Sound shows signs of enrichment illustrated by the elevated biomass of the benthic fauna.

The Belt Seas and Öresund This area, including the North Belt Sea, the South Belt Sea (the Kiel Bay and the Femer Belt) and the three narrow straits, the Little Belt, the Great Belt and Öresund (Fig. 7), connects the 455

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Figure 7 The Belt Sea, bottom areas where seasonal hypoxia has occurred several times in August to October 1994 –2000. Distribution of bottom oxygen concentrations between 1.4 ml l−1 and 2.8 ml l−1 (dashed; Little Belt ∼890 km2, Great Belt ∼2750 km2 and South and East Lolland ∼720 km2) and between 0 ml l−1 and 1.4 ml l−1 (shaded; Little Belt ∼650 km2, Great Belt ∼270 km2 and South and East Lolland ∼350 km2). (Based on information from DMU 1996, 1997, 1998, 1999, 2000, Fyns Amt 2000).

Kattegat with the Baltic Sea. A north-ﬂowing surface current carries brackish Baltic water out into the Kattegat and a compensating near-bottom current carries saline water from the Kattegat into the Baltic Sea with varying frequency and magnitude. The average depth in the Belt Seas and Öresund is about 10–20 m and maximum depth is about 40 m. A pycnocline is found around 15 m depth. Most environmental information on Danish coastal waters originates from the Danish Ministry of the Environment (DMU). The load of nutrients from Denmark to the marine areas has been reduced since the 1980s by approximately 80% for P and by 15% for N, mainly because of improved sewage treatment. The P concentrations in the Danish surface waters have decreased signiﬁcantly in the period 1989–97, concurrently with improved 456

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treatment of sewage water. However, the noted reduction of N concentration during the same time period in the inner Danish waters corresponded to a decrease in precipitation and runoff. Especially in 1996 and 1997, the precipitation was low, giving low N inﬂux these years. In accordance with decreased nutrient concentrations, a reduced primary production in early spring and summer was observed during these two years. In summer, the production was reduced from about 500 mg C m−2 day−1 in the 1980s to about half of that in 1996–97 (DMU 1998, 1999). However, the warm summer in 1997 caused extensive oxygen depletion in Danish estuaries, which led to an enhanced release of nutrients from the sediments and blooms of phytoplankton in several estuaries (DMU 1999). Trend analysis of the development of the oxygen concentrations in the bottom water in late summer/autumn showed a decrease from the 1970s to the end of the 1980s in the Belt Sea below 17.5 m depth. The largest decrease rate (0.14 mg l−1 yr−1) was noted in the southern Belt Sea. Estimates from the Kiel Bay indicate that the decrease in oxygen concentration started in the end of the 1950s (Babenerd 1991). In the period 1989–97, the negative trend seemed to have stopped in the Öresund, where a signiﬁcant increase was noted in the autumn oxygen concentration. However, in the southern Little Belt and the southern Belt Sea, the concentrations continued to decrease. Oxygen concentrations below 2 ml l−1 have been found anually in the whole area below 17.5 m depth from the 1970s to the end of the 1990s (DMU 1998, 1999) (Fig. 7). Henriksson (1969) investigated macrobenthic fauna in Öresund between 1961 and 1963. The benthic communities were then characterised as those typically found in polluted areas with certain species of polychaetes as particular indicators: Nereis diversicolor, Capitella capitata and Scoloplos armiger. He also detected hypoxic conditions (10–25% O2 saturation) during the summers of 1961–63. Öresund is exposed to sewage water, among other sources, from the treatment plants in Copenhagen and Malmö (DMU 1999). Long-term investigations at one station in the Great Belt (depth 38 m) and one in Öresund (depth 17 m) during the time period 1979–96, showed high values of both biomass and abundance in the beginning of the 1980s and in the mid 1990s. Between these periods a minimum of both parameters was observed. For the whole period (1979–96) there has not been any change in biomass. However, the composition of the fauna has changed. In general, polychaetes, molluscs and echinoderms increased during the 1990s compared with the 1980s, while crustaceans decreased signiﬁcantly (DMU 1998, 1999). Oxygen depletion was found not to be the most important regulating factor for the observed change in the benthic fauna in the Great Belt and Öresund. Oxygen concentrations in the Great Belt and Öresund were measured to be below 2 ml l−1 only occasionally, in the years 1987, 1989 and 1992 during the study period (1979–96), and no effects on the fauna (biomass or species composition) could be observed the following years (1988, 1990, 1993). However, in other areas (for example the Little Belt), changes in oxygen concentration may explain the changes of the benthic fauna (DMU 1998). The benthic fauna in the Great Belt and Öresund was suggested to change with changes in food supply (DMU 1998). According to DMU (1998), inputs of nutrients in coastal areas mainly come from freshwater land runoff. Therefore, land runoff has, in their investigation, been used as an indirect measure for plankton blooms and associated food availability for the benthic fauna. The biomass of the fauna and land runoff was found to correlate with a one to two-year lag (DMU 1998, 1999). The benthic fauna in Danish fjords and near-coastal areas showed large ﬂuctuations in both biomass and abundance in periods of about 5–8 yr (DMU 1999). Climate and 457

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environmental factors (food availability and oxygen concentration) were likely explanations for the great ﬂuctuations. In contrast to the fauna in open water areas, the fauna in Danish fjords and near-coastal areas showed no synchronisation in ﬂuctuation of population dynamics among stations. Josefson & Rasmussen (2000) found a positive effect of nutrient load on benthic biomass in Danish estuaries during the period 1989–95. Between 1976 and 1989 the change in oxygen concentrations in the bottom water was analysed at six stations around the Danish island Funen (Fyn, Fig. 7) (Anonymous 1991, 2000). The analyses were made from June to October, because oxygen depletion is most frequent during this time. The area showed diminishing average oxygen concentrations, and a decreasing trend in annual minimum oxygen concentrations. The decline was greatest in the northern Little Belt, Ringsgård Basin and Langeland Sound (from about 8 to about 4 mg l−1). The falling oxygen concentrations each season were probably caused by a combination of factors, of which nutrient load and water exchange were probably the most important. In other areas around Funen, the Mid Little Belt and Ærø Basin (south of Funen), low oxygen concentrations (average around 4 mg l−1) have been noted during the whole period, mainly because of the local topography and hydrography, which cause long periods of stratiﬁcation of the water masses every year. Minimum seasonal oxygen concentrations of <2 mg l−1 have been frequently reported during the whole time period in these areas. Longterm investigations (1930–86) in the South Little Belt indicate a 5-fold increase of areas with oxygen depletion (Anonymous 1991). In general, a decline of species sensitive to oxygen depletion was found. In the Little Belt (depth 10–35 m), a decline of the following species has been noted; Ophiura albida, Terebellides stroemi, Echinocardium cordatum, Macoma calcarea and Nephtys ciliata (Anonymous 1991). These species are all known to be sensitive to oxygen depletion. At the same time, species less sensitive to oxygen depletion have increased by a factor of two to ﬁve times (from about 400–500 ind. m−2 to >2000 ind. m−2 ), for example Halicryptus spinulosus, Scoloplos armiger, Harmothoe sarsi, Heteromastus ﬁliformis, Nephtys hombergii, Corbula gibba and Arctica islandica. In summary, the water exchange in this area partly counteracts oxygen depletion. However, the large nutrient input and high sedimentation of organic matter explain the decline in oxygen concentration, and the changes in benthic community structure.

Kiel Bay Kiel Bay (Fig. 7) is located southwest of the Great Belt and the Femer Belt, and has a mean water depth of 16–17 m and a maximum depth of about 30 m. Because of the topography and the Coriolis force, a considerable amount of deep water passing through the Great Belt enters Kiel Bay. Kiel Bay, therefore, functions as a sediment trap in the straits between the North Sea and the Baltic Sea. The inﬂowing deep water sometimes contains a large amount of organic matter (Nehring 1971) and is often oxygen-poor, which can contribute to the observed increased frequency of oxygen depletion in this area (Weigelt 1990). Weigelt (1990) summarised the oxygen conditions in the deep water of Boknis Eck (26–28 m) in the western Kiel Bay between 1957 and 1985. Oxygen depletion has increased signiﬁcantly since the end of the 1950s and was especially important during the 1970s and the beginning of the 1980s (oxygen saturation was 3– 43% in July and August on 11 of 12 occasions). The deepwater oxygen content started to decrease earlier in the year than normally, too early to be caused only by stagnation and by local biological oxygen demand (Weigelt 1990). Also, Babenerd (1991) reported an increased oxygen deﬁciency in the water below the pycnocline 458

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Kiel

Kiel Bay

Mecklenburg Bight

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Figure 8 The Kiel Bay and Mecklenburg Bight, distribution of hypoxia (0–1.4 ml l−1, dashed, ∼630 km2) and anoxia (shaded, ∼2750 km2) in the bottom water in September 1981 (based on information from Weigelt & Rumohr 1986).

at about 14 m depth in the Boknis Eck during the same time period (from about 8 g O2 m−3 to about 4 g O2 m−3 on average). Oxygen consumption rates in summer had increased by a factor of two to three between 1957 and 1985, because of an increased oxygen demand resulting from increased supply of organic matter. Primary production more than doubled during the same time period (Babenerd 1991). During these periods, negative effects on the benthic fauna were observed. Macrofaunal species that are tolerant of organic pollution have become much more abundant since 1981 (Weigelt 1990). In late summer 1981, mass mortality of the benthic fauna below the halocline (>20 m) occurred because of extraordinary and wide-ranging oxygen depletion in Kiel Bay (Fig. 8). Hydrogen sulphide was formed and about 30 000 t of macrofauna died (97%). Before this event, 60 species had been reported in the central part of Kiel Bay, and 20 of these were abundant (Arntz 1971, 1980). During the 1981 oxygen depletion event, only a few species survived: the calms Arctica islandica, Astarte spp. and Corbula gibba and the priapulid Halicryptus spinulosus. The fauna, however, recolonised the area rapidly. In late summer of 1983, a similar but not so severe oxygen depletion occurred and caused mortality of Abra alba and other species below the halocline (Weigelt & Rumohr 1986). Locally restricted faunal breakdowns are common for a semi-enclosed area like Kiel Bay. Such a wide range oxygen depletion with subsequent mass-mortality of fauna had, according to Weigelt & Rumohr (1986), never been reported before. Further, they concluded that the causes for such events seem to be related to climatic changes in connection with general eutrophication. Thus, Kiel Bay is highly vulnerable to environmental perturbations, as illustrated by the frequent periods of mass-mortality in combination with hypoxia. 459

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The Kattegat The southern Kattegat (Fig. 9) has a mean depth of 23 m. A strong halocline is found at about 15 m between the brackish surface water of Baltic origin and the deeper, more saline water (∼34) from the Skagerrak. The Kattegat is connected with the Baltic through narrow straights and shallow bottom areas in the Öresund and the Belt Seas. As a consequence, the bottom water circulation in the southern Kattegat is reduced, which make this area susceptible to seasonal hypoxia in late summer and autumn (Rydberg et al. 1990). Input of N and P to the Kattegat and the Skagerrak is estimated to have increased during the twentieth century by factors of four to six and less than eight, respectively (Rosenberg et al. 1990). During 1971 to 1990, both surface and deep water showed an increasing trend for N and P during the winter (Andersson 1996). Rosenberg et al. (1996) reviewed the environmental quality of the Kattegat and the Skagerrak and found that eutrophication and toxic substances have caused large-scale environmental changes and other effects in these sea areas. In the early 1980s, widespread oxygen deﬁciency in the near-bottom water with accompanying effects on the benthos was probably recorded for the ﬁrst time in the Kattegat and the Belt Seas (Rosenberg 1985). On the Swedish southeast coast of the Kattegat, the Laholm Bay, the extension of bottom areas with hypoxic water varied between years in the period 1980 through 1990 (Rosenberg 1992, Rosenberg & Loo 1988). The largest areas with hypoxia (oxygen concentrations <2 ml l−1) were recorded in the autumns of 1986, 1988 and 1990 with the maximum area affected estimated to be c. 5000 km2 (Fig. 9a). This seasonal hypoxia was suggested to be correlating with a more than 3-fold increase in input of N during the 1960s and 1970s via rivers entering Laholm Bay (Rosenberg et al. 1990). In the 1990s, fewer oxygen recordings were obtained in that area and information is therefore less detailed.

Southeast Kattegat The benthic fauna in Laholm Bay was reduced during periods of hypoxia, both at depths around the halocline and deeper. For example, at a station at 22 m the following species were eliminated in the autumn of 1988 (O2 <1 ml l−1): Diastylis rathkei, Amphiura ﬁliformis, Ophiura albida, Euchone papillosa, Scoloplos armiger and Terebellides stroemi. None of these species had re-established in that area two years later, except possibly Ophiura albida. Other species like Arctica islandica, Corbula gibba, Phoronis muelleri, Heteromastus ﬁliformis and Myriochele sp. survived the period of low oxygen concentrations (Rosenberg et al. 1992). These authors stated that the benthic fauna in that area tolerated oxygen concentrations between 0.5 ml l−1 and 1.0 ml l−1 (8–15% saturation). Pearson & Rosenberg (1992) constructed an energy budget of the Laholm Bay area. The budget emphasised the strong benthic–pelagic coupling at all depths, and the importance of horizontally advected food supply into the area. Suspension feeders dominate in shallow waters. At water depths down to 10 m they have the capacity to ﬁlter the whole water volume twice a week (Loo & Rosenberg 1989), thus reducing the phytoplankton biomass. At greater depths, deposit feeders take a slightly larger proportion of the energy. As indicated above, in some years the benthic sub-system balances just at the edge of mortlity and survival. Should oxygen concentrations drop to lower levels (below ∼0.7 ml l−1) or be present over longer periods (months), the energy-ﬂow through the benthic compartment will be greatly reduced. The result will then be accumulation of sedimentary carbon prior to the initiation of anaerobic pathways through prokaryote organisms. 460

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Figure 9 The Kattegat, bottom areas with seasonal hypoxia, examples from: a) the east Kattegat, September 1988 (<2 ml l−1, dashed, ∼5650 km2; <1 ml l−1, shaded, ∼3850 km2; based on information from Rosenberg et al. 1992), b) the Århus Bay, 1981 (0.3–2.8 ml l−1, dashed, ∼1770 km2; based on information from Fellesen 1992), c) the Limfjord, August–October 1997 (<1.4 ml l−1, shaded, ∼440 km2; based on information from DMU 1998).

Demersal ﬁsh species and crustaceans have been severely affected during hypoxic periods in the Kattegat. In the mid-1970s, Bagge & Munch-Petersen (1979) noted that the catches of Norway lobster, Nephrops norvegicus, increased during moderate hypoxia at 30 to 50 m depth in the central Kattegat. This correlation was repeated in the southeast Kattegat in the 461

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early 1980s (Baden et al. 1990b). The reason for the enhanced catches was that the lobsters left their burrows when oxygen saturations dropped below ~20%. Following the enhanced catches, however, these authors reported declining catches in hypoxic areas. During the severe hypoxia in 1988, it seemed that N. norvegicus was eliminated from at least the south part of the Kattegat. Also, the scientiﬁc demersal ﬁshery failed and very few ﬁsh were caught in that area. Instead, dying invertebrates were caught in trawls at a rate of 200 to 400 kg h−1 (Baden et al. 1990a). Thus, many infaunal species had left their burrows in the sediment and were available to trawls when on the sediment surface. Behaviour to stretch into the water column where a little more oxygen may be available, has been reported in several species: ophiuroids stand on their arm tips and Norway lobsters on tip-toe (Baden et al. 1990a). Arms of the brittle-star Amphiura ﬁliformis are important in the diet of the dab, Limanda limanda (Pihl 1994). Regeneration rates of nipped arms are, however, greatly reduced during hypoxia. Nilsson & Sköld (1996) estimated that a potential of approximately 100 t of regenerating arms, as possible food for ﬂatﬁsh, were lost in the southeast Kattegat during hypoxia in the autumn of 1988. From 1990, investigations in the southeast Kattegat have been less extensive. The benthic fauna at one station at 20 m outside Laholm Bay has improved during the period 1993 through 1998 and seemed then to have responded to improved oxygen conditions in that particular area (Göransson 1999). In 1999, however, the author found that the number of species per 0.5 m2 had declined from 58 to 37, but no clear cause for the drop was identiﬁed.

Southwest Kattegat Wastewater and nutrients enter Århus Bay at the Danish east coast, and possible effects on the fauna are monitored almost annually. In 1981, an extraordinary oxygen deﬁciency (concentrations not reported) killed most of the benthic fauna below the halocline (Fig. 9b). Recolonisation followed and the fauna was restored about four years later (Fellesen 1992). In the 1990s, the conditions at the sediment surface seem to have been rather stable, but the area is still considered to be eutrophic.

Danish estuaries in the Kattegat The environmental conditions in Danish estuaries are monitored regularly by the Danish Ministry of the Environment (DMU). The summary below is mainly from such reports (DMU 1997, 1998). Most of these estuaries are shallow, <10 m deep. The load of N in 14 Danish estuaries has been positively correlated with the benthic biomass, and the eutrophication of these estuaries was dependent on the water residence times (Josefson & Rasmussen 2000). Several estuaries and coastal areas experience low oxygen concentrations annually in August and September. The severity of the hypoxia was not correlated with that in the open Kattegat, for example, in 1994 most estuaries went hypoxic but the oxygen concentrations in the Kattegat and the Belts were higher than normal for that same period (Josefson & Rasmussen 2000). In a review of Danish estuaries, Conley et al. (2000) noted that hypoxia and anoxia are frequent phenomena related to eutrophication. Effects on benthic communities were described primarily as increased dominance of ﬁlter-feeding molluscs and ascidians. 462

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It is stated in the DMU reports that generally there is no trend in benthic biomass or abundance in Danish coastal waters over the period 1979 to 1997. However, examples of negative effects on the benthic fauna in several estuaries and fjords are given below. Some of the branches in the Limfjord have seasonally hypoxic bottom water occurring most years. In 1996, the hypoxic area was estimated at 17% of the total bottom area, and in 1997 at 30% (Fig. 9c). In the latter year, about 400 000 t of blue mussels, Mytilus edulis, were killed. The Mariager fjord, which has a sill at the entrance, is classiﬁed as susceptible to hypoxia. In 1997, severe hypoxia in August–September caused mass-mortality of invertebrates and ﬁsh. Also the bays on the north of Sjælland, the Roskildefjord and the Isefjord, experience hypoxia most years. The latter fjord was especially affected in the years 1994, 1995 and 1997, when extensive mortality of the benthos occurred.

Open Kattegat In 1984, Pearson et al. (1985) revisited 24 stations (depths 12–69 m) that were sampled by Petersen in 1911–12 in the open Kattegat. At 16 of these stations, the biomasses recorded in 1984 were less than those found early in the century, and at six stations very much less. The changes were partly attributable to decreases of the echinoid Echinocardium cordatum. In general, ophiuroids and polychaetes increased in biomass, whereas echinoids and molluscs decreased. Changes in functional groups showed that suspension feeders and carnivores increased in dominance, whereas deposit feeders decreased. In 1984, the majority of species were smaller in size compared with the situation in 1911–12. In 1989, Josefson and Jensen (1992) re-sampled 13 of the same stations. That study conﬁrmed the small average sizes of the fauna. The greatest reductions in animal size were found in areas were hypoxia had been present. Results from these two investigations strongly suggested that the benthic communities, particularly in the southern Kattegat, were affected by eutrophication-induced hypoxia in the 1980s. Later, a comparison of the faunal biomass over the period 1979 to 1997 was made for the Kattegat within the Danish monitoring programme, and no temporal changes were observed (DMU 1998). Danish measurements of oxygen in the period 1989 to 1997 showed a signiﬁcant increase in minimum concentrations (ﬁgures not given) for the south Kattegat (DMU 1998). In summary, since the early 1980s, the benthic fauna has been negatively affected by seasonal hypoxia in the southern Kattegat and in many Danish and Swedish coastal areas. This has also caused negative effects for the demersal ﬁshery. The severity of the effects varied between years, possibly because of climatic variations.

The Skagerrak The open Skagerrak (Fig. 10) has a mean depth of 230 m and is connected with the North Sea. The eastern part is stratiﬁed because of the inﬂuence of brackish water of Baltic origin, whereas in the western part oceanic water extends to the surface. The Swedish and Norwegian coastal areas are, in many parts, affected by eutrophication and low oxygen concentrations in the bottom water. In the eastern, coastal Skagerrak there was a signiﬁcant increase in inorganic nutrients during winters from 1971 to 1990 (Andersson 1996). The deepest part is >700 m, but hypoxia has never been recorded there (Aure & Dahl 1994), and the benthic fauna seemed to be undisturbed in 1992–94 (Rosenberg et al. 1995). 463

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Idefjord

Figure 10 Swedish and Norwegian fjords, bottom areas with aperiodic hypoxia (<2 ml l−1, dashed) and anoxia (shaded): a) Swedish Skagerak fjords (Gullmarsfjord ∼25 km2 in 1997; Havstensfjord, Byfjord and Koljöfjord ∼140 km2 in 1997–1998; based on information from Nilsson & Rosenberg 1998, 2000), b) Oslofjord (Bunnefjord ∼100 km2 in 1973–1999, Vestfjord ∼250 km2 in 1973–1999, Eastern outer Oslofjord ∼125 km2 in 1980–1994 and Idefjord ∼80 km2 in 1968–1991; based on information from NIVA 1995, 2000).

The Swedish coast This coastline is rocky with many small islands and fjord-like inlets. In fjords in the central part, signiﬁcant negative trends in minimum oxygen concentrations (periodically as low as 0 ml l−1) in the bottom water were found over the period from the 1950s or 1960s up to 1984 at 12 sites (20–62 m depth) (Rosenberg 1990). In some fjords the hypoxia seems to occur 464

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seasonally, whereas in other more enclosed areas it is irregular. Changes in the benthic communities are described below, generally from south to north. The benthos in the Stigfjord, Ellösfjord and Åbyfjord (Fig. 10a), which have some direct connection to the open coast, was investigated at the same 14 sites in 1976 and 1986 at depths between 7 m and 27 m. Between these years, signiﬁcant reductions were recorded in total macrofaunal abundance and biomass (Josefson & Rosenberg 1988). The authors suggested that the changes could be a result of large-scale eutrophication in the area, as these fjords do not have any local riverine input of nutrients. The Stigfjord and the Ellösfjord were re-visited again in the autumn–winter 1997–98 at stations with 7 to 16 m water depths. Conditions had deteriorated compared with the two earlier dates, and only a few species and individuals were found at most stations and one was azoic (Nilsson & Rosenberg 1998). Oxygen concentrations were not reported. In fjords further away from the coast, for example, the Havstensfjord (Fig. 10a), sediment proﬁle imaging (SPI) demonstrated effects of hypoxia in sediments deeper than 25 m depth (Nilsson & Rosenberg 1997). The oxygen conditions had worsened in 1997 and negative effects on the bottoms were recorded in SPIs from 6 m depth (Nilsson & Rosenberg 1998). A sill at about 12 m is situated between the Havstensfjord and the Byfjord. The basin inside the sill has been anoxic, probably for centuries. There is no benthic fauna below the oxycline at about 15 m, and number of species and biomass gradually decline towards this depth (Rosenberg 1977). An energy-ﬂow model has been presented by Rosenberg et al. (1977), which showed that most of the energy in this eutrophic fjord was channelled through shallow water epifauna, mainly blue-mussels, Mytilus edulis. Species being rather abundant in hypoxic waters were Phoronis muelleri, Ophiodromus ﬂexuosus, Polydora ciliata, Aporrhais pespelecani and Corbula gibba (Rosenberg 1977). The Gullmarsfjord has a sill at 40 m and a deep basin with a maximum depth of 118 m. Oxygen concentrations in the deeper part of the fjord show a seasonal variation with minimum values before the annual water renewal usually occurring in spring (Lindahl, pers. comm.). In the winter 1979–80, the oxygen concentration dropped to 0.2 ml l−1 at 118 m depth, the benthic macrofauna was eliminated (Josefson & Widbom 1988), and the meiofauna was negatively affected (Austen & Widbom 1991). In 1997, the annual water renewal did not occur, which was the ﬁrst time on record. As a consequence, the oxygen concentrations remained <10% saturation for more than half of that year at depths <100 m, and for a month at <80 m. Responses of the benthic sedimentary habitat and the benthic macrofauna were investigated bimonthly by SPI and grab samples (Nilsson & Rosenberg 2000). The faunal diversity, abundance and biomass were reduced at depths below 80 m and eliminated below 105 m. Critical oxygen saturation for survival was found to be ≈10% (0.7 ml O2 l−1). In situ SPIs showed that before reaching critical oxygen concentrations, the tube-building polychete Melinna cristata increased the length of its tubes up to a maximum of almost 10 cm above the sediment surface. This was interpreted as a way to reach water richer in dissolved oxygen. The authors found a strong correlation between changes in the sedimentary habitat quality and changes in diversity, abundance and biomass. In early 1998, the bottom water was re-oxygenated and colonisation began. Species particularly tolerant of low oxygen concentrations were the deposit feeders M. cristata, Heteromastus ﬁliformis, Thyasira sarsi and Thyasira equalis. Tunberg and Nelson (1998) analysed benthic data from 12 to 20 yr at 10 stations in the Gullmarsfjord and outside that area. They grouped the data into three depth intervals and suggested that the macrobenthic biomass and abundance oscillated in 7.9-yr intervals in 465

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relation to climatic variability. The time period analysed may be too short (and the covariation between pooled stations lacking) to prove a temporal cycle of variation. Climatic changes have, however, a signiﬁcant impact on the marine ecosystem and on the frequency and magnitude of effects related to eutrophication. For example, the failure of bottom water renewal in the Gullmarsfjord in 1997 was most probably driven by large-scale climatic factors, but oxygen consumption of the bottom water should primarily be correlated with sedimenting organic matter. In January 1998, the benthic habitat was studied along 50 km of the coastline north of the Gullmarsfjord (depths 13–33 m) by SPI at 60 sites, and macrofauna was collected at 14 stations previously sampled in 1987 and 1990 (Nilsson & Rosenberg 1998). The benthic habitat was classiﬁed as disturbed in the whole investigated area. There were <5 species 0.3 m−2 at six stations and the abundance was <15 ind. 0.1 m−2 at seven stations. Species diversity was signiﬁcantly lower in 1998 than in 1990 at ﬁve stations. The causes for these, at least, local effects on the benthos are unknown, but could be localised hypoxia. The number of stations studied was low, but the disturbance calls for further detailed investigations. The long and narrow Idefjord at the border between Sweden and Norway has been greatly affected by hypoxia and anoxia from at least the 1960s and up to the early 1990s (Fig. 10b). The benthic fauna were negatively affected and eliminated along a distance of at least 20 km (Dybern 1972, Rosenberg 1980). The main cause for this disturbance was the efﬂuent from the sulphite pulp mill in Halden. When that operation stopped in 1991, the water quality improved and the benthic fauna of the Idefjord has been restored in most areas, but periodic hypoxia appears east of Halden at depths >20 m (Afzelius 1996).

Norwegian coast Since 1927, oxygen concentration, temperature and salinity have been measured every year at 31 stations along the Norwegian Skagerrak coast. At 10 m and 20 m depths a signiﬁcant negative trend was observed from the 1960s to the 1990s (Johannessen & Dahl 1996). In the bottom water, the authors detected a decline during the 1970s with minimum oxygen saturations <10% at six of the stations. No corresponding changes in meteorological or hydrographical variables were found, and it was concluded that the declining oxygen concentrations were most likely caused by increased nutrient load of the coastal waters. Similarly, Aure et al. (1996) found increased oxygen consumption in the sill basins of nine Norwegian fjords in the Skagerrak, and these authors attributed this change to increased large-scale eutrophication. A generally increasing trend in benthic faunal abundance (maximum ∼3000 ind. m−2) was recorded at 464 stations at the Norwegian south coast over the period 1980 to 1997. This trend was interrupted in 1988 to 1990, which was suggested to be an effect of the bloom of the toxic ﬂagellate Chrysochromulina polylepis (Rygg 1998). The author suggested that the numerical increase was correlated with increased inputs of nutrients and organic matter during the 1990s. The enrichment of the Oslofjord in Norway has created a gradient of oxygen stress that has worsened through time. An inner part of the Oslofjord, the Bunnefjord (Fig. 10b), seemed unaffected by hypoxia about 100 yr ago, when shrimps, Pandalus borealis, were caught in that area. By 1915, however, one station in the deeper area was depauperate, but other stations in the middle Oslofjord, the Vestfjord, had well developed benthic communities (Petersen 1915). Oxygen concentrations in the deep parts (>75 m) of the Vestfjord declined from 466

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2.8 ml l−1 in the 1930s, to 1.7 ml l−1 in the 1960s, and to 0.9 ml l−1 in the 1970s (Mirza & Gray 1981). These authors reported that the benthos was affected in the hypoxic areas, and the annual re-oxygenation of at least part of the bottom water supported a pioneering successional-stage community in that area. Later studies showed an improvement of the benthos in the Vestfjord from 1985 and 1993, correlated with improved oxygen concentrations in the bottom water between these years from ∼1.0 ml l−1 to 3.5 ml l−1 (Olsgard 1995). Faunal changes in the inner Oslofjord since 1952 have been summarised in detail by Beyer & Indrehus (1995). The largest river in Scandinavia, the Glomma (Fig. 10b), has a water ﬂow exceeding 1000 m3 s−1 during May and June. The mouth of the Glomma is at the eastern part of the outer Oslofjord. The annual inputs of N and P are approximately 16 000 t and 500 t, respectively (Magnusson & Sørensen 1993). South of the Glomma is the Singlefjord, where minimum oxygen concentrations <1.0 ml l−1 have been recorded in areas 30 m and 50 m deep. The benthic fauna was negatively affected at several stations in 1980 to 1982 in the central fjord, but improved conditions were recorded in 1990 and 1994 (Berge et al. 1996). The authors attributed the changes to reduced pollution and improved oxygen concentrations (trends not given).

Open Skagerrak In 1985, Rosenberg et al. (1987) sampled the benthic macrofauna at 23 stations (10 to 327 m depth) in the open Skagerrak and the outer Oslofjord, which were investigated with similar methods by Petersen in 1914. In 1985, the biomass was found to be signiﬁcantly higher by a factor of 1.8 compared with that in 1914. Signiﬁcant increases were recorded for polychaetes and echinoderms. The community composition had changed considerably between the two dates, with general increases in the deposit feeders Echinocardium cordatum, Abra nitida and Thyasira spp. and the suspension–deposit feeder Amphiura ﬁliformis. The authors suggested that the biomass increase might have been caused by a general organic enrichment, particularly in the outer Oslofjord. During 1972 to 1988, Josefson (1990) monitored the benthic macrofauna at 12 stations in the Skagerrak and two in the northern Kattegat (18 to 300 m depth). Total biomass showed a linear increase by a median factor of 1.8, primarily attributable to echinoderms and polychaetes. Josefson (1990) suggested that the investigated area was affected by organic enrichment. In summary, the Skagerrak showed clear signs of enrichment on the sea beds, which in many areas had led to reduced bottom fauna but in some other areas the biomasses had increased.

Discussion Temporal and spatial development of hypoxia and/or anoxia In this review 20 Scandinavian sea areas have been identiﬁed where bottom water hypoxia and/or anoxia have occurred since 1950 (Table 1). In all these areas negative effects on the 467

468

1950–1999 1982–1993? 1985–1995 1990s c. 150 300 30 30

71 239 90 146

380 20 20 150 211 c. 200

20 57–70 8–22 20 20 20

Mean biomass (g ww m−2)

60 11 25 10

440 54 150 80

170** 890 1 860 2 150 3 850 1 300

70 000 2 330 3 000 1 200 2 500 1 000*

Severe hypoxia and/or anoxia or laminated (km2) sediments

9 3 0,8 0,3 Total 3283

31 13 14 12

65 18 37 323 812 260

1400 147 45 24 50 20

Missing biomass (ton *103)

* crude estimations after SMHI homepage (www.smhi.se), Swedish Environment Protection Agency homepage (www.environ.se). ** crude estimations after Kube (1996) and Powilleit & Kube (1999). Bold text indicates areas with more or less permanent hypoxia or anoxia.

Stockholm Archipelago Himmerfjärden St Anna Archipelago Åland Archipelago

ARCHIPELAGO AREAS

Limfjord Swedish Skagerrak fjords Oslofjord Idefjord

1997 1997–1998 1973–1999 1968

1994 1981 1981 1980–1999 1988 1981

Pomeranian Bay Kiel Bay Mecklenburg Bight Belt Sea Kattegat (East) Århus Bay

FJORDS

1965–1997 1969–1985 1996 1965–1997 1965–1997 1988, 1992, 1994

Period

Baltic Proper Gulf of Finland Eastern Gulf of Finland (Neva Bay) Gdansk Basin Bornholm Basin Arkona Basin

OPEN WATER AREAS

Area

Stockholm Vatten 2000, Rosenberg & Diaz 1993 Elmgren & Larsson 1997 estimated estimated

Rosenberg 1980, DMU 1998 Rosenberg 1972 NIVA 1989 Rosenberg 1980

Osoweicki 1991 Weigelt & Rumohr 1986 Weigelt & Rumohr 1986 DMU 1999 Pearson & Rosenberg 1992 Fellesen 1992

Rosenberg 1980, Jonsson et al. 1990 see text Seire 1991 estimated see text DMU 1998 and text

References for estimating mean biomass

Table 1 A crude estimate of “missing macrofaunal biomass” is presented for each sub-area, where the benthic faunal biomass has been reduced or eliminated. The “missing biomass” is an approximation of the biomass that could have been present during normoxic conditions in each sub-area, and the calculations are based on biomasses that were present prior to hypoxia and/or anoxia or of biomasses from adjacent areas. Thus, the table allows only a crude comparison between areas affected by oxygen deﬁciency. The periods of hypoxia and/or anoxia were selected based on when data in the literature were available. The sum of “missing biomass” is an estimate of some of the “worst” years. Some references for hypoxic periods or benthic biomass are given in the table; others are found in the text.

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EUTROPH I CATI ON, OXYGEN DEFI CI EN C Y A N D B E N T H I C F A U N A

bottom fauna have been reported or indicated. The areas are either enclosed and/or have sills with reduced transport of bottom water, or they are stratiﬁed by vertical differences in salinity and sometimes also in temperature. Norwegian and Swedish fjords and some Danish inlets are typically enclosed coastal areas with limited water exchange. The Baltic proper is a large sea area, but with narrow and shallow connections to the Kattegat. Thus, it resembles a large fjord with a narrow mouth. The pycnocline in the Baltic is situated at about 80 m depth, and below this hypoxia and anoxia occur almost permanently. In the southern open Kattegat, a strong halocline is found most of the year between 10 m and 20 m depth. The halocline is formed between the brackish surface water of Baltic origin (salinity ∼16) and deeper oceanic water (salinity ∼32). In stratiﬁed areas of the Kattegat, the Öresund and the Belt, hypoxia is seasonal and occurs during late summer and autumn. The Skagerrak fjords are both enclosed and stratiﬁed, and here bottom water hypoxia and sometimes also anoxia may last for longer periods, sometimes several years. Thus, low oxygen concentrations occur both in coastal and open seas, which can be either stratiﬁed and/or enclosed. The temporal development of low oxygen concentrations in Scandinavian bottom waters is shown in Figure 11. This analysis focuses on the period 1950 to 2000. Low oxygen concentrations (<2 ml l−1) have developed earlier, but the effects on the benthic fauna have been limited compared with the effects found in the second half of the twentieth century. An example of long-term measurements, and with a clear decline in oxygen concentrations around the mid-1950s, was illustrated by Fonselius (1969) from the Baltic proper (Fig. 2). The earliest recordings of severe bottom water hypoxia and anoxia are from about 1950 in the Baltic Sea and from Skagerrak fjords (Fig. 11). In some other areas it appears that the introduction of bottom water hypoxia occurred around 1960 and in others, generally more shallow areas, about 20 yr later. Thus, bottom water and sedimentary areas with low oxygen concentrations have been spreading during the later part of the twentieth century. The low oxygen concentrations in the Idefjord were caused by discharges of pollutants and organic matter from the sulphite pulp mill in the inner parts, at Halden. When that factory closed down in early 1990, conditions improved and most of the bottom water has been oxygenated since then. The Baltic proper and Gulf of Finland have been hypoxic and/or anoxic since the early 1960s (Fig. 11), which is partly because of reduced water exchange with the Kattegat. Fonselius (1969) reported only one period of stagnation before 1940. However, oxygen concentrations in the Gulf of Finland seem to have improved for periods over the last 15 yr. The reason for this is probably mainly because of the weakening of the pycnocline, followed by improved mixing of the water masses, and, as a consequence, increased oxygen concentration in the bottom water. Enclosed areas such as the Stockholm Archipelago seem to have had hypoxic bottom water since the 1950s, whereas in the comparatively open Åland Archipelago, the local hypoxic bottom waters appeared about 30 yr later (Fig. 11). The severity of the hypoxia and/or anoxia and the time it lasts in an area is partly related to climatic factors (Zorita & Laine 2000). For example, rainy winters will release more nutrients from farmland, which may increase the plankton biomass and sedimentation of organic material. Temperature and bottom water transport and mixing are other factors that may vary between years and affect bottom water oxygen concentrations. The vertical position of the halocline can vary between seasons, which in the Kattegat has been demonstrated to have an impact on the distribution of hypoxic water (Rosenberg et al. 1992). Thus, even though it is predictable that hypoxia will occur seasonally in these areas, the frequency and magnitude may vary. In the fjordic areas, the temporal extension of hypoxia and /or anoxia 469

(45–50 m) (26–28 m) (17–40 m) (15–50 m) (10–45 m)

Arkona Basin

Kiel Bay

Belt Sea

Kattegat

Idefjord

470 (12–28 m) (25–50 m)

St. Anna Archipelago

Himmerfjrd

(10–30 m)

I

1960 I

1970

?

I

I

1980 1990

?

?

I

2000

?

?

Figure 11 Estimated temporal distribution of permanent (____), seasonal (- - -) and irregular (__ -) hypoxia (<2 ml l−1) in some Scandinavian bottom waters from 1950 to 2000. Due to lack of detailed information, the distributions for some years have been estimated.

Finnish Archipelago

(>10 m)

(20–50 m)

Stockholm Archipelago

Åland Archipelago (inner)

(10–24 m)

Limfjord

(15–118 m)

(80–92 m)

Bornholm Basin

Swedish Skagerrak Fjords

(80–118 m)

Gdansk Basin

(70–165 m)

(70–120 m)

Gulf of Finland

Oslofjord (Vestfjord)

(70–250 m)

Baltic Proper

I

1950

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EUTROPH I CATI ON, OXYGEN DEFI CI EN C Y A N D B E N T H I C F A U N A

is, however, less predictable because sometimes the water renewal does not occur and at other times it may be incomplete. Despite lack of detailed information about historic records of bottom water oxygen concentrations, it is obvious that areas experiencing oxygen deﬁciency in Scandinavia have been expanding. Further, the formation of hydrogen sulphide in connection with periods of anoxic bottom water, has increased over time (Unverzagt 2001). This is in agreement with similar global changes as described for many other eutrophic coastal areas (Diaz & Rosenberg 1995). Thus, there is clear evidence that in urban coastal areas and where excess fertilizers are used, eutrophication is often associated with hypoxic bottom water. During the last century, the input of N and P to Scandinavian waters has increased by factors of four to six and more than eight, respectively (Rosenberg et al. 1990). This, along with possibly altered climatic and related hydrographic conditions, is the main reason for the spread of hypoxic and/or anoxic bottom areas in Scandinavian waters.

Losses of benthic macrofaunal biomass The benthic macrofauna has been reduced or eliminated in several of the areas with hypoxic bottom water and in all areas with anoxia. In the following account an attempt is made to estimate the loss of macrofaunal biomass (Rosenberg 1980) by estimating the areas of soft sediments where the oxygen concentration has been <1 ml l−1, i.e. severe hypoxia (Table 1). These areas have been treated as having low or no biomass (Diaz & Rosenberg 1995). Bottom areas in the Baltic with laminated sediments have been treated in the same way, as no large infaunal organisms have been present in these areas for some years. These estimates are indeed crude, but they are conservative. However, for some areas that do not experience hypoxia every year or where it differs in extent between years, the largest hypoxic distribution has been used. Mean missing biomasses for each area have been estimated from periods prior to the development of hypoxia or for adjacent areas. The largest loss of biomass has occurred in the Baltic, estimated at 1400 × 103 t in the Baltic Proper and 147 × 103 t in the Gulf of Finland (Table 1). In the Baltic Proper a biomass loss of that order has been more or less permanent for decades. The estimated benthic biomass losses in the Danish Belts and the Kattegat have also been signiﬁcant. Here, the mean biomasses are greater and this contributes to a greater total loss. The extent of biomass losses in the east Kattegat is uncertain, but demersal trawling in the autumn of 1988 showed that the benthic fauna was greatly affected in the whole area (Baden et al. 1990a). The hypoxia is seasonal in the Belt areas and the Kattegat, with a variable distribution of hypoxic bottom water between years. Thus, these areas are colonised by benthic fauna each year and the secondary production may be high during years with better oxygen conditions. The missing biomass in areas deﬁned as open sea areas was estimated at more than 3000 × 103 t ww, in Kattegat and Skagerrak fjords at about 70 000 t, and in the Baltic archipelago areas more than 10 000 t; in total ≈3300 × 103 t (Table 1). This estimate is only valid when the worst years for hypoxia and/or anoxia are selected and summarised. If instead, bottom areas are selected where hypoxia and/or anoxia have been present over several years during the last decades, the estimate of missing benthic biomass is ≈1500 × 103 t. It is difﬁcult to speculate about the potential secondary production on these sediments with a depth range from 15 m to 250 m. If it is assumed that the production equals the biomass (Banse & Mosher 1980), and that the production would be consumed by predators annually, 471

KARIN KARLSON, RUTGER ROSENB E R G & E R I K B O N S D O R F F

then at least 1000 × 103 t could have been consumed by demersal ﬁsh, with a conversion factor of 10% that could have resulted in a total ﬁsh production of 100 × 103 t. It is considered this that is a very crude estimate. This great loss of benthic fauna may be partly compensated for by an increased eutrophication-induced benthic biomass in areas outside the hypoxic zone. In such comparatively better ﬂushed areas, a high input of organic carbon may result in a higher benthic biomass as shown for some areas in the Baltic (Cederwall & Elmgren 1980) and the Skagerrak (Rosenberg et al. 1987), and a higher pelagic ﬁsh yield (Elmgren 1989).

Tolerance to oxygen depletion by benthic species In this paper the emphasis of discussion is on the tolerance to oxygen depletion by species in the Baltic Sea. Reference is also made to a similar analysis of other benthic species by Diaz & Rosenberg (1995). One of the most important structuring factors for benthic faunal communities is oxygen concentration. Species show a great variation in sensitivity (Table 2) to oxygen depletion and different strategies have been developed among species to cope with this unfavourable situation. In addition, during anoxia hydrogen sulphide can be formed in the sediment. Hydrogen sulphide is in general extremely toxic to aerobic organisms. The toxic effect is inhibitory interaction with various enzymes, especially cytochrome oxidase and blood pigments (Evans 1967). The degree of tolerance to this toxic compound varies among species, and variations in tolerance to sulphide between populations within a species have also been found, e.g. Macoma balthica (Jahn & Theede 1997) and Nereis diversicolor Table 2 Examples of soft sediment macrofaunal species in the Baltic Sea grouped after degree of sensitivity to hypoxia according to literature data. Numbers in brackets refer to the referenses listed below. Faunal Groups

Resistant to severe hypoxia

Resistant to moderate hypoxia

Sensitive to hypoxia

POLYCHAETA

Heteromastus ﬁliformis (7,8,17)

Pygospio elegans (25)

BIVALVIA

Arctica islandica (8,12,13,19,20) Astarte borealis (6,8,12,19) Halicryptus spinulosus (6,8,11,12,19) Saduria entomon (1,2,8,10,16)

Capitella capitata (7,17) Harmothoe sarsi (1,6,7,18) Nereis diversicolor (5,8,17,20,21) Marenzelleria viridis (23,24) Scoloplos armiger (7,8,17,18,22) Macoma balthica (1,6,7,9,14,15) Mya arenaria (20)

Corophium volutator (25)

Diastylis rathkei (8) Monoporeia afﬁnis (1,2,3,4) Pontoporeia femorata (1,2,3)

PRIAPULIDA CRUSTACEA

ECHINODERMATA

Macoma calcarea (6,7)

Amphiura ﬁliformis (8,21)

References: 1) Laine & Seppänen 2000, 2) Johansson 1997a, 3) Johansson 1997b, 4) Sandberg-Kilpi et al. 1999, 5) Vismann 1990, 6) Tulkki 1965, 7) Leppäkoski 1969, 1971, 8) Diaz & Rosenberg 1995, 9) Mölsä et al. 1986, 10) Hagerman & Szaniawska 1988, 1990, 11) Oeschger & Vetter 1992, 12) Oeschger 1990, 13) Oeschger & Storey 1993, 14) Jahn et al. 1997, 15) Jahn & Theede 1997, 16) Hagerman & Vismann 1993, 17) Pearson & Rosenberg 1978, 18) Andersin et al. 1978, 19) Weigelt & Rumohr 1986, 20) Theede et al. 1969, 21) Hagerman 1998, 22) Schöttler & Grieshaber 1988, 23) Fritzsche & Von-Oertzen 1995, 24) Hahlbeck et al. 2000, 25) Blomqvist & Bonsdorff 1986.

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(Röhner et al. 1997). As an example, higher tolerance was shown to have developed among Macoma balthica from a sulphidic habitat in the Gulf of Gdansk compared with specimens from areas with lower sulphide concentrations (Jahn & Theede 1997). The geographical distribution of the benthic fauna in the Baltic Sea is, thus, affected by the oxygen concentrations, but also other factors, particularly salinity (Bonsdorff & Pearson 1999). The extended areas of hypoxia and anoxia in Scandinavian waters during the past 50 yr have affected the benthic fauna in many ways. Among the most serious effects are the large bottom areas in the Baltic Sea that are totally depleted of benthic macrofauna or have greatly decreased faunal biomasses. Also, modiﬁcations in the faunal communities have occurred. A shift in faunal composition to more tolerant species has been recorded in several areas. For example, polycheates have become more common than other taxa in the Bornholm and Gdansk basins during the 1960s compared with the early 1950s (Andersin et al. 1978). Many polychaetes are considered to be opportunists and are thus favoured in unstable environments, for example, those where seasonal and irregular hypoxia and/or anoxia occur (Pearson & Rosenberg 1978). Other examples are the numerical decrease of Monoporeia afﬁnis in several areas, for example in the Åland Archipelago between the 1970s and 1990s (Bonsdorff et al. 1991), in the Finnish Archipelago since the end of the 1980s (Mattila 1993, Jumppanen & Mattilä 1994) and in the Gulf of Riga during the 1990s (Cederwall et al. 1999). Contemporary, increases of other more tolerant species have occurred (e.g. Macoma balthica and Marenzelleria viridis). Further, a decreased biodiversity is generally found in areas with hypoxia. Oxygen is considered to be a key parameter for the number of species present (Diaz & Rosenberg 1995, Bonsdorff et al. 1996, Bonsdorff & Pearson 1999). For example, contemporaneously with reduced oxygen concentration, the number of species in the Bornholm Basin has decreased from approximately 20 to about six when comparing the ﬁrst half of the twentieth century with the 1980s and 1990s (Osowiecki & Waszocha 1996). Similarly, in the Arkona Basin a reduction in number of species from 16 to ﬁve has occurred when comparing the beginning of the 1980s with the beginning of the 1990s (HELCOM 1996). Another effect of hypoxia may be a reduction in mean body size, which has, for example, been found among the majority of species in the Kattegat during the 1980s compared with the 1910s (Josefson & Jensen 1992).

Different strategies to cope with hypoxia and/or anoxia and hydrogen sulphide Different strategies have been developed among species to cope with anoxia and hydrogen sulphide (Hagerman 1998). Mobile fauna can escape areas with oxygen depletion and hydrogen sulphide formation. Adult benthic macrofauna from soft sediments, however, in general have only limited swimming capabilities, and have therefore to cope with hypoxia in other ways. Many benthic animals irrigate their burrows with water from above the sediment to keep them oxidised. Short-term behavioural defence can be to emerge from the burrow to the sediment surface in order to reach higher oxygen concentrations in the water above the surface, with Amphiura ﬁliformis and A. chiajei as examples (Rosenberg et al. 1991). Further, laboratory studies have shown that a strategy by the amphipods Monoporeia afﬁnis and Pontoporeia femorata to cope with reduced oxygen conditions, could be to minimise the costs associated with obtaining oxygen, as well as the risk of predation, by 473

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moving little on or above the sediment when critical oxygen concentrations prevail (Johansson 1997a). Bivalves can close their valves to reduce sulphide exposure for short times. However, during longer-term exposure to anoxia and hydrogen sulphide, anaerobic metabolism, sulphide resistance and sulphide detoxiﬁcation will be needed. Different strategies are, however, variably developed among species. The phyla Mollusca, Annelida and Priapulida have species with relatively high anaerobic capacity and high sulphide resistance (Table 2). Crustaceans, on the other hand, are in general not particularly tolerant of hypoxia or hydrogen sulphide; examples are Monoporeia afﬁnis and Pontoporeia femorata (Johansson 1997b, Sandberg-Kilpi et al. 1999). The isopod Saduria entomon, on the other hand, is one exception among crustaceans. It burrows in sandy and muddy sediments but does not construct tubes with connection to the sediment surface. However, it has a good tolerance to hypoxia by efﬁcient extraction of oxygen from the environment (down to <0.25 to 0.5 ml l−1) (Hagerman & Szaniawska 1988) and an effective anaerobic metabolism, apparently unique among crustaceans, which makes it possible to survive extended periods of anoxia, up to 300 h (Hagerman & Szaniawska 1990). Further, this species has a high capacity to detoxify hydrogen sulphide by oxidation or by hydrogen sulphide binding to metallic ions in the hepatopancreas (Vismann 1991). Nereis diversicolor occurs in the Baltic Sea as far north as to the entrance of the Gulf of Finland and to the east coast of the Gulf of Bothnia. It can be found in different types of sediment but prefers silty sediments with a high organic content (Kristensen 1988). Laboratory studies have shown that N. diversicolor is able to survive in hypoxia with sulphide present for at least three weeks. However, they changed their behaviour, stopped feeding and some individuals moved to the sediment surface (Vismann 1990). A sulphide oxidation activity has been found and especially in the ﬂuid of the intestine, which could have physiological importance in sulphide detoxiﬁcation, as N. diversicolor can ingest sediment (Vismann 1990). For comparison, N. (= Neanthes) virens, which prefers oxidised sandy substrata, has signiﬁcantly lower intestine sulphide oxidation activity (Vismann 1990). N. diversicolor is, thus, able to utilise sediments with higher hydrogen sulphide concentrations than N. virens. Marenzelleria viridis is a recent invader in the Baltic Sea (ﬁrst seen in southwestern Baltic Sea in 1985), which lives in both shallow and deep locations as well as in sandy and muddy habitats, and it has a deeper vertical distribution in the sediment than any other benthic macrofaunal species in the northern Baltic Sea. Consequently, it is frequently subjected to periods with restricted oxygen availability. Laboratory studies have also shown that M. viridis is well adapted to a life in a sulphidic environment and has developed different physiological abilities to cope with this harsh environment (Hahlbeck et al. 2000). There are, for example, indications that the worm exhibits an efﬁcient use of oxygen (Fritzsche & VonOertzen 1995, Schiedek 1997), since energy production through anaerobic metabolism ﬁrst starts at severe hypoxia (<1 ml−1) and also that this polychaete is able to detoxify hydrogen sulphide through oxidation during severe hypoxia (Hahlbeck et al. 2000). However, during the oxygen depletion in Pomeranian Bay 1994, M. viridis was able to survive moderate hypoxia, but died after exposure to severe hypoxia (Kube & Powilleit 1997). The period with reduced oxygen concentration probably exceeded the hypoxia capacity of M. viridis. It has been estimated that its resistance to oxygen deﬁciency ranges between 21 h and 290 h depending on temperature and salinity (Fritzsche & von-Oertzen 1995). According to ﬁeld observations, the polychaetes Harmothoe sarsi and Scoloplos armiger have a high hypoxic tolerance (Andersin et al. 1978, Laine et al. 1997). Both species can be 474

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found in oxygen concentrations down to 0.5 ml l−1. Harmothoe sarsi have been reported to be the only species in some areas with oxygen depletion and Scoloplos armiger is a species often found as an early coloniser after a period of anoxia (Andersin et al. 1978, Leppäkoski 1969, 1971, Tulkki 1965). Is has been demonstrated that S. armiger is able to maintain fully aerobic metabolism down to an oxygen concentration of ~1.0 ml l−1, and at an oxygen concentration of ≤0.5 ml l−1 a partly aerobic metabolism can be retained (Schöttler & Grieshaber 1988). However, it seems to have only restricted capacity to reduce its metabolism and exhibits only moderate resistance to anoxia. The two bivalves, Arctica islandica and Astarte borealis and the priapulid, Halicryptus spinulosus, common in the western and southern Baltic Sea, are examples of species with an extraordinary high resistance to severe hypoxia and anoxia (Table 2). Laboratory studies have shown that they have among the highest LT50-values under oxygen deﬁciency known among marine invertebrates (Oeschger 1990, Theede et al. 1969). Arctica islandica and Astarte borealis, occur on silty and muddy sediments and can survive anoxia for months, which is achieved by a well-adapted anaerobic metabolism and depression of the metabolic rate. The clams are able to reduce their metabolism by up to 40% during the ﬁrst day of oxygen depletion when they turn to anaerobic metabolism, and after prolonged anoxia, energy release decreased to <1% of the aerobic rate (Oeschger 1990). Halicryptus spinulosus lives in loosely-structured burrows in muddy sediments down to 30 cm depth and is able to cope with considerable amounts of sulphide (sulphide concentration exceeding 200 µM) by possessing several survival strategies (Oeschger & Vetter 1992). External sulphide can partly be immobilised by an external barrier, the outer mucus barrier, and hydrogen sulphide that has entered the body can be detoxiﬁed. Arctica islandica, Astarte borealis and Halicryptus spinulosus together with Corbula gibba were the only macrofaunal species that survived the extraordinary oxygen depletion in Kiel Bay 1981 (Weigelt & Rumohr 1986). Arctica islandica and Astarte borealis can together make up ~90% of the biomass in the bay (Weigelt & Rumohr 1986). The persistence in hostile environments shown by the three species stresses their ecological importance. Their presence might facilitate re-colonisation by other species when the situation improves. Macoma balthica is common in the Baltic Sea. Adult specimens can be found in oxygen concentrations <1 ml l−1 and have been reported to survive in almost complete anoxia for several days (Mölsä et al. 1986). However, the minimum oxygen level needed by settled larvae seems to be considerably higher (Mölsä et al. 1986). Further, it has been shown that the amount of accumulated sulphide in the tissue is dependent on individual size (Jahn et al. 1997). Higher concentrations are found in small specimens compared with large, and a reduction in numbers of small clams has been found in sediments with high sulphide concentrations. This indicates that efﬁcient detoxiﬁcation is possible only after M. balthica has reached a certain size. Consequently, an environment with reduced oxygen concentration may maintain adult specimens but prevent larval settlement.

Consequences for the ecosystem The input of nutrients to the Baltic and the Kattegat has increased since the 1970s, which has led to increased concentrations of N and P in the water (Rosenberg et al. 1990, Wulff et al. 1990, Bonsdorff et al. 1997a,b). A positive correlation between annual primary production and the load of total N has been shown for some different sea areas including the Baltic Sea 475

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(Jonge et al. 1994). This has led to an increased sedimentation of organic material on deeper sea beds, which has been demonstrated for the Kattegat and Belt areas (Wassmann 1990). In the Baltic proper, the deposition of organic matter has increased 1.7 times from the 1920s to the 1980s (Jonsson & Carman 1994). Increased input of organic material to the sediments leads to increased oxygen consumption in the near-bottom water and the superﬁcial sediment, particularly in late summer and autumn (Rydberg et al. 1990). Thus, the increased eutrophication is an important ecological factor for the increased distribution of hypoxic and anoxic sediments. Bottom areas in the Baltic with laminated sediments have increased rapidly since 1960 and covered about 70 000 km2 in 1990 (Jonsson et al. 1990). Laminated bottom areas develop because of lack of bioturbation. Thus, these areas have no macrofauna buried in the sediment for long periods. Most of these areas correspond with the distribution of severe hypoxia or anoxia in the bottom water (see previous sections). In areas where benthic macrofauna is negatively affected by oxygen deﬁciency, sediment reworking processes and burrow systems are lacking or reduced in frequency or number. This will have consequences for biogeochemical processes, such as N ﬂuxes. For example, nitriﬁcation and denitriﬁcation require aerobic and anaerobic sites, respectively. In animal burrows these might occur in juxtaposition, and biogeochemical transformations are consequently mediated by faunal bioturbation and irrigation activity, and structures (e.g. burrows) in the sediment (Andersen & Kristensen 1991). Wulff et al. (1990) suggested that, in the Baltic Sea, denitriﬁcation at the redoxcline in the sediment is quantitatively much more important than denitriﬁcation in the water column. Thus, an increased distribution of the redoxcline in sediments because of infaunal structures would increase the removal of N from the marine ecosystem. At present, with eliminated or reduced benthic fauna, eutrophication may instead be a self-accelerating process because of decreased denitriﬁcation and increased release of P (Baden et al. 1990a, Pitkänen & Välipakka 1997) from the sediments to the water column in hypoxic and/or anoxic areas. During reduced conditions in the superﬁcial sediment, manganese will leave the sediment and may affect benthic species. It has been shown that Nephrops norvegicus can have 20 times higher concentrations of manganese in the gills during periods of hypoxia than in normoxia (Eriksson & Baden 1998). Bottom habitats that have reduced, sulphidic sediment with overlaying hypoxic water are likely to be covered by sulphur bacteria, Beggiatoa spp. This has been shown in sediment proﬁle images (Rumohr 1990, Rosenberg & Diaz 1993), and core samples from the Baltic (Jonsson & Jonsson 1988). Beggiatoa spp. get their energy from oxidising H2S, and their biomass can be 5 to 20 g ww m−2 as recorded in the Limfjord, Denmark (Jörgenssen 1977). Thus, instead of macrofaunal and meiofaunal biomasses, the sulphur bacteria may contribute living organic matter on the sediment surface. They also have a signiﬁcant effect on the biogeochemical cycles at the sediment/water interface as discussed for the Baltic Sea by Rosenberg and Diaz (1993). Persistent and seasonal benthic hypoxia affects the energy ﬂow processes in the ecosystem. The effect may vary from collapse of secondary production and extreme enhancement of bacteria to no effects on production but pulsed transfer of benthic biomass to upper-level consumers (Diaz & Rosenberg 1995). In an energy ﬂow model of the southeast Kattegat, the importance of the benthic sub-system was emphasised, particularly the suspension feeders (Pearson & Rosenberg 1992). The authors calculated that hypoxia could reduce predation of crustaceans on infauna by 23%. In that same area, Baden et al. (1990a) discovered dramatic changes on the sediments during demersal trawl ﬁshing in 1988. Instead of catching ﬁsh, a 476

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high diversity and biomass (200–400 kg h−1) of benthic infuna was captured, which had emerged to the surface from their burrows in the sediment. In the Baltic Sea, pelagic ﬁsh such as herring and sprat seem to have beneﬁted from eutrophication, whereas the consequence for cod has been the opposite (Breitburg et al. 2001). Cod eggs need a salinity of >11 to be buoyant (Nissling & Westin 1991), and such salinities are only found in deeper waters in the Baltic. During a survey in 1987 to 1990, it was found that 50% to 95% of the cod eggs ended up in hypoxic or anoxic deep water. As the eggs need >2 ml l−1 to hatch, they do not develop in hypoxic water (Ohldag et al. 1992). The development of the ﬁshery and the size of the cod stocks was described and discussed in relation to the abiotic conditions in the Baltic Sea by Bagge et al. (1994). They showed that the spawning area corresponded very well with the permanent hypoxia found in the eastern Gotland Basin, Gdansk Deep and Bornholm Deep. After a maximum recruitment of cod (800 million) in the mid 1970s, the annual number decreased drastically and was estimated to have reached 50 million by 1990, the lowest number recorded up to that date. From the models of nutrient-related ecosystem effects developed by Caddy (1993), it seems plausible that most of the energy transfers in the future will take place in the pelagic system and the ecological importance of the benthic system will decline further. Energy ﬂows in the Baltic Sea are further discussed by Elmgren (1984, 1989).

Future perspective The ratio of watershed-to-basin area for the Baltic is 4.2 : 1 (Caddy 1993). This suggests that it may take a long time to decrease the nutrient input from a drainage area of that enormous size. Several measures have been introduced over the last 10 to 15 yr to reduce the input of nutrients (Gren et al. 2000). Data from the ofﬁcial statistics in Sweden show that the input levels have levelled off and been similar since the elevations occurred up to the 1970s. Thus, there is at the moment little hope for a large-scale reduction of the marine eutrophication and extent of areas with oxygen deﬁciency. Also in a global perspective, once marine sediments have become hypoxic, there is little immediate hope to return these areas to pre-hypoxic conditions (Diaz & Rosenberg 1995). However, if the input levels could be decreased signiﬁcantly, perhaps to about that present in 1950, many bottom areas may reach minimum oxygen concentrations of more than 2 ml l−1. Such areas can be colonised rapidly and develop into normal benthic communities typical for a particular habitat within a few years.

Acknowledgements We thank the numerous colleagues and authorities around the Baltic Sea, who kindly provided us with information and references for this review. The ﬁnal selection of data and information, and any mistakes in interpretation are entirely our own. We thank Katri Aarnio for help on the northern Baltic coastal areas, Ari Laine for help and viewpoints on the open Baltic Sea, Sergej Olenin for comments on the manuscript, and Pia Nyman & Cecilia Rönnberg for editorial assistance. This work is part of the Swedish research program MARE (Marine Research on Eutrophication). 477

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Wassmann, P. 1990. Calculating the load of organic carbon to the aphotic zone in eutrophicated coastal waters. Marine Pollution Bulletin 21, 183–187. Weigelt, M. 1990. Oxygen conditions in the deep water of Kiel Bay and the impact of inﬂowing salt-rich water from the Kattegatt. Kieler Meeresforschungen 33, 1–22. Weigelt, M. & Rumohr, H. 1986. Effects of wide-range oxygen depletation on benthic fauna and demersal ﬁsh in Kiel bay 1981–1983. Kieler Meeresforschungen 31, 124–136. Westman, P., Wastegård, S., Schoning, K., Gustavsson, B. & Omstedt, A. 1999. Salinity change in the Baltic Sea during the last 8500 years: evidence, causes and models. Swedish Nuclear Fuel and Waste Management Co, Technical report TR-99-38. Wulff, F., Stigebrandt, A. & Rahm, L. 1990. Nutrient dynamics of the Baltic Sea. Ambio 19, 126 – 133. Zettler, M. L. 1997. The newcomer Marenzelleria viridis (Verrill 1873), its development and inﬂuence on the indigenious macrozoobenthos in a coastal water of southern Baltic. In Proceedings of the 14th Baltic Marine Biologists Symposium, E. Ojaveer (ed.). Pärnu, Estonia: Estonian Academy Publishers, 280–296. Zmudzinski, L. 1978. The evolution of macrobenthic deserts in the Baltic Sea. In XI Conference of Baltic Oceanographers 2, 780–794. Zorita, E. & Laine, A. 2000. Dependence of salinity and oxygen concentrations in the Baltic Sea on large-scale atmospheric circulation. Climate Research 14, 25– 41.

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Oceanography Biology: Annual 2002, M AM MALS and I N IMarine NTERTI DAL an AND MAReview R I T I ME E C40, O S491–608 Y S T E MS © R. N. Gibson, Margaret Barnes and R. J. A. Atkinson, Editors Taylor & Francis

MAMMALS IN INTERTIDAL AND MARITIME ECOSYSTEMS: INTERACTIONS, IMPACTS AND IMPLICATIONS P. G. MOORE University Marine Biological Station Millport, Isle of Cumbrae, KA28 0EG Scotland e-mail: [email protected]

Abstract Although much information exists on the role of non-human mammals (both terrestrial and marine) on seashores and in maritime terrestrial environments, it is widely scattered in the literature. This review represents the ﬁrst attempt to draw this disjointed information together. The reasons for past neglect are several: in some habitats, e.g. temperate rocky shores, such interactions are genuinely of relatively minor signiﬁcance and have typically been ignored; the focus of marine mammal research on land has generally been ethological, not synecological; and the activity of small mammals, being predominantly nocturnal, passes largely unnoticed by (mostly) diurnal ecologists. Oceanic and inshore island populations are most at risk from introduced mammals. Cats, dogs, rats and mink are important predators of seabirds. Grazers such as goats and sheep cause coastal habitat degradation. Eradication of goats is especially important on islands and in faunas with high proportions of endemic species. Attention is focused below on mammalian involvement in ecological processes in a range of ecosystems: viz. salt marshes and shore meadows, sand dunes and machair, intertidal sand ﬂats, shingle beaches, rocky shores, caves, cliffs, lava tubes, mangroves, seagrass and kelp beds and ice-edge habitats. Ecological observations on Marsupialia, Insectivora, Chiroptera, Lagomorpha, Rodentia, Cetacea, Carnivora, Sirenia, Perissodactyla, Artiodactyla and Primates are presented. Contemporary issues involving coastal mammals (particularly livestock husbandry) are collated, namely marine pollution by nutrient outwash (eutrophication), microbiological water quality, radioactivity and the impact of climate change and sea-level rise (particularly on ice-edge and saltmarsh habitats). Any exploitation of shores by terrestrial mammals has to be intermittent and cyclical on a tidal basis. Evictions of terrestrial mammals from feeding or nesting sites in coastal ecotone habitats may follow tidal or storm events, and can be locally catastrophic (generating carcasses for scavengers). Expanding mammalian populations on islands run the risk of exhausting food resources and starving, especially in winter. Certain marine mammals, however, like manatees, dugongs and bottle-nosed dolphins, may exploit the intertidal zone during its periodic immersion at high tide. The role of mammals, particularly grazing livestock, is much greater in saltmarsh and sand-dune ecosystems (including machair habitats) than in the intertidal zone. Occasional strandings of cetaceans or deaths of seals, particularly neonates, provide intermittent bounties of energy for opportunistic scavengers like foxes, hyenas, jackals or bears (even humans in isolated situations), depending on location. Other coastal energy bounties come in the form of turtle and iguana nests raided for eggs by foxes, raccoons and mongooses. Bearded pigs may also intercept hatchlings on their way to the sea. Fringing densely wooded hinterlands, shorelines and beaches form unobstructed highways for larger animals and may, historically, have played an important role in facilitating their post-glacial dispersal. The importance of productive

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shorelines for foraging by grazing, predatory and scavenging mammals will be magniﬁed along bleak hinterlands, for example, desert coasts. The sea otter and giant kelp story has become an ecological paradigm reinforcing the need for better understanding of the ramiﬁcations through ecosystems of changes to the status of keystone species. Bats and ﬂying foxes, for instance, are key species in mangrove ecosystems, as essential pollinators. Some bats catch ﬁshes. Accumulated bat droppings may be important sources of nutrients under roost sites. Chemical interactions (social signalling) between mammals in coastal situations are largely unknown. This and several other areas of relative ignorance (suitable for modern techniques like radio tracking, appropriately designed enclosure/exclosure experimentation, habitat restoration and pest eradication) are highlighted as requiring future research. Habitat management and sustainability at this important boundary would be improved if the effects of, and implications for, mammals were considered.

Introduction Most shore ecologists study invertebrates and/or algae but there is also an extensive literature on birds (e.g. Ranwell & Downing 1959, Prater 1972, Lifjeld 1984, Puttick 1984, Feare & Summers 1985, Vader 1994, Wootton 1995, Colwell & Dodd 1997, Nacken & Reise 2000) on both hard-shore, soft-shore and maritime pasture ecosystems, and on ﬁshes as predators and grazers in the littoral zone (Gibson 1969, 1982, Jones 1992, Horn et al. 1998, Ojeda & Munoz 1999). To date, however, there has not been any attempt to collate literature on the ecological role of non-human mammals in the same arena, perhaps because our own collective and individual coastal impact, can be so overwhelming (Fogden 1971, Summers et al. 1975, Hall & Moore 1986, Aleem 1990, Gerodette & Gilmartin 1990, David 1991, Malik et al. 1997, Vitousek et al. 1997, Kiely & Myers 1998, Reimchen 1998, Simeone & Schlatter 1998, Attrill et al. 1999, Blanco et al. 1999, Brown & Taylor 1999, Buckingham et al. 1999, Schiel & Taylor 1999, Aastrup 2000, Eckrich & Holmquist 2000, Leseberg et al. 2000, Rey & Schiavini 2000). On occasion, though, some coastal Carnivora (notably bears, seals, tigers and dingoes) do get their own back on humans. Even befriending small wallabies on Rottnest Island, Western Australia not infrequently results in being bitten (McDonagh 1992). Human opthalmomyiasis caused by the ﬁrst instar larva of the reindeer warble ﬂy Hypoderma tarandi has been reported from coastal regions in northern Norway (Kearney et al. 1991). Coastal horseﬂies (Tabanidae) in different parts of the world can transmit trypanosomes (Otte et al. 1994) or Spiroplasma spp. (Konai et al. 1997). Horses may be important amplifying hosts for Ross River virus which causes epidemic polyarthritis in mankind in coastal New South Wales, Australia (Vale et al. 1991) and which is transmitted by saltmarsh mosquitoes (Aedes vigilax). Feral dogs in the Galapágos Islands are important reservoirs of ﬁlarial heartworm that has a high human incidence in the archipelago (Barnett & Rudd 1983). Histoplasmosis, caused by the yeast Histoplasma capsulatum, is a possible consequence of contact with bat guano accumulations, e.g. in caves inland (Suzaki et al. 1995) inter alia (see Bulmer & Bulmer 2001), so persons venturing into coastal bat caves should take precautions against dust inhalation. The role of marine mammals in aquatic ecosystems was reviewed by Bowen (1997) and their biology has been detailed recently by Reynolds & Rommel (1999), but mammals receive little mention in standard works on seashore ecology (Dakin 1953, Macnae & Kalk, 1958, Lewis 1964, Stephenson & Stephenson 1972, Morton & Miller 1973, Barrett 1974, 492

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Moore & Seed 1985, Fish & Fish 1996). Indeed, Schäfer (1972) stated that except for whales, seals, and other marine forms, mammals rarely have contact with the sea. Passing reference to mammals, however, was made by Ricketts & Calvin (1968) in their (Hedgpethupdated) classic work Between Paciﬁc Tides. Many people’s experience of mammals on shores may extend little beyond entertaining children to seaside donkey (Equus asinus) rides. The reasons for this lack of awareness (Peterson 2001) are several. In some habitats, e.g. temperate rocky shores, such interactions are genuinely of relatively minor signiﬁcance and have typically been ignored (Navarrete & Castilla 1993). The focus of marine mammal research on land has generally been ethological, not synecological and the activity of small mammals, being predominantly nocturnal, passes largely unnoticed (Thompson 1931). Notwithstanding the above, more attention has been paid to larger terrestrial mammals (especially domesticated livestock) in saltmarsh and sand-dune ecosystems (Ranwell 1972). The interactive effects of mammalian grazers (hares, rabbits, cattle, sheep) on salt marshes and maritime pastureland can also be a prominent factor in facilitating avian grazers, like brent geese (Branta bernicla), or ground-probing shorebirds (Colwell & Dodd 1997, van der Wal et al. 2000). Communities are complex entities and even apparently minor players on a particular stage may have a surprising number of interdependencies (e.g. Ratnaswamy & Warren 1998, Courchamp et al. 2000, Laffaille et al. 2000). In certain coastal ecosystems mammals do play a key role. For instance, dugong exploitation of seagrass beds has been likened to cultivation (Preen 1995a, Bowen 1997). Sea otters have a profound impact on population dynamics of a whole cascade of species within the giant kelp ecosystem (Estes & Palmisano 1974, Dayton 1975, Estes 1996). The sea otter and giant kelp story has become an ecological paradigm reinforcing the need for better understanding of the ramiﬁcations through ecosystems of changes to the status of keystone species (but note Estes et al. 1998, Watt et al. 2000). Paine (2000) has recently highlighted past neglect of the role of grazing ungulates in community structuring processes in terrestrial ecosystems in comparison with the strong grazer interactions seen on marine rocky shores. Daiber (1982) has written extensively on the animals of the tidal marsh, mainly from an American perspective, in a treatise that is certainly a notable exception to the general rule. The following considerations are intended to supplement and complement his excellent treatment. Subsequently Moors & Atkinson (1984) have reviewed the impact of introduced mammal species (mongooses, mustelids, pigs and foxes) on seabirds (cf. Chimera et al. 1995, Kelsey & Collins 2000). The review that follows begins with consideration of certain general topics relating to mammalian involvement in the structure and function of coastal ecosystems. Detailed treatments of interactions and impacts of mammalian taxa then follow. Finally, global implications regarding mammalian occupancy of littoral and maritime ecosystems are addressed.

Mammals and islands Oceanic and inshore island populations are most at risk from introduced mammals (Ebenhard 1988, Mauchamp et al. 1998, Hobson et al. 1999, Dowding & Murphy 2001). It is most undesirable that alien predators be deliberately introduced into predator-free off-shore islands (Hockey 1996), for whatever motive (as was done with hedgehogs on South Uist, 493

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outer Hebrides, with disastrous consequences for breeding wader colonies, see p. 516). Equally, the desirability of elimination of inadvertently introduced mammalian pest species from offshore islands, where feasible, cannot be overstated (Fitzgerald et al. 1991, Chapuis et al. 1994). Programmes to remove introduced mammals from seabird islands and to reduce the possibility of future introductions are needed, both to restore seabird populations and to preserve biodiversity. New Zealand was that most special of special cases; a land lacking native mammals other than bats (Elton 1958). Worldwide, the ten most commonly introduced mammals are the rabbit, three species of rats, the house mouse, and domesticated pigs, cattle, cats, dogs and goats (Ebenhard 1988), and the European hare (Lepus europaeus) has become as cosmopolitan as any other truly wild mammal (Elton 1958). Cats, dogs, raccoons, rats, foxes and mink are seabird (especially chick and egg) predators (Krogh & Schweitzer 1999, see below), grazers such as goats and sheep cause coastal habitat degradation (Pimm 1991, LeCorre & Jouventin 1997, Mauchamp et al. 1998, Simeone & Schlatter 1998, Courchamp et al. 1999, Alvarez-Cardenas et al. 2000, Bigazzi & Selvi 2000), as do pigs (Waithman et al. 1999), and rabbits destroy habitat and compete with hole-nesting seabirds (Monteiro et al. 1996, McChesney & Tershy 1998). In the Azorean archipelago, breeding Procellariiformes tended only to occur on small rat-free islets (Furness et al. 2000). The reproductive capacity of rodents, in particular, means that they pose considerable risk to island-breeding seabird colonies (as do feral cats) (Zino et al. 2000). Their eradication from important seabird colonies requires considerable and persistent effort, careful planning and adequate resourcing (Taylor & Thomas 1993). It may have unwanted side-effects if poisoned baits are deployed (see p. 523). Impacts from introduced mammals have been most severe on islands with no native mammalian predators (Ebenhard 1988, McChesney & Tershy 1998). Rabbits are perhaps not so much successful colonizers of islands as environmentally tolerant animals that survive well wherever they are placed. Undoubtedly the main vertebrate colonizers of islands are humans; the rabbit is merely one of their lesser associates in the subjugation of the environment (Flux 1994). In situations where cats and rabbits are both present on islands, priority is frequently given to control of cats. However, the presence of rabbits can allow an increased predator population which can lead on to extinction of indigenous (and less well adapted) prey species, and increase the difﬁculty of predator control. Simultaneous control of both introduced species has therefore been advocated (Courchamp et al. 1999, 2000). Rabbits can disrupt management of sand-dune communities by breeding in response to reductions in stock numbers designed to beneﬁt biodiversity (Dargie 1997). Mammals like rats, foxes, mink, feral cats and goats are opportunistic feeders. Eradication of goats is especially important on islands and in faunas with high proportions of endemic species (Parkes 1990, 1993, Keegan et al. 1994). It is notable that the goat was banned by the native St Kildans a few years after its introduction because it interfered with the sea birds on the cliffs which were the islanders principal food source (Steel 1975). Conservation conﬂicts, however, can emerge. Thus among the French islands of the South Indian Ocean, Amsterdam Island is the richest in endemic species, but its indigenous terrestrial ecosystem has been drastically modiﬁed, not least by the herd of introduced feral cattle (Bos taurus), which expanded from a population of ﬁve in 1871 to 2000 in 1988 (Micol & Jouventin 1995). All cattle deaths there in 1988 were due to starvation, with cows being more susceptible (47.7%) than bulls (18.3%) (Berteaux 1993). At that time, the herd was the main threat to the endangered indigenous species but it was also one of 494

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the very few feral herds of B. taurus anywhere in the world. A compromise was decided upon between the urgent need to protect the island on the one hand, and the scientiﬁc interest of the herd on the other, and the island was divided by a fence (Micol & Jouventin 1995). Work on Australian islands has shown a very strong relationship between the presence of exotic mammalian species and the local extinction of native mammals. The introduction of exotic mammals to these islands should be prevented, and any introductions that occur should be eradicated immediately (Burbidge et al. 1997). Systematic study of the islands off Western Australia has revealed that there are 254 islands 100 ha or larger in area, 90% of which are in tropical seas. Of these 254 islands, all but eight are free of the red fox (Vulpes vulpes) and 16 are free from cats (Felis catus). Depending on distances from the mainlands considered to be out of range of swimming abilities of these species, over 100 islands offer potential sites for establishing populations of native mammals not otherwise represented on islands and now with restricted distribution on mainland Western Australia (Abbott 2000). Similarly, Erwin et al. (2001) recommended predator (Vulpes vulpes and raccoon Procyon lotor) removal on certain of the Virginia barrier islands (USA), since environmental managers were fast running out of options for conservation of colonies of waterbirds, particularly beach-nesting terns (Sterna spp.) and black skimmers (Rynchops niger) in the face of marked increases in the island ranges of these predators in recent years. Ever since Charles Darwin focused attention on the divergence of organisms between different islands within the Galápagos archipelago (Darwin 1838– 43), such places have exerted a fascination over evolutionary biologists. Mammalian species richness on islands is highly correlated with island size (Millien-Parra & Jaeger 1999). After insularisation, substantiated island biogeographic theory has it that small islands lose more species than do large islands. Thus susceptible taxa are those now found only on large islands. Risky lifehistory traits among primates as determined from Asian Sunda Shelf islands were large body mass, low population density, large annual home range and low maximum latitude (Harcourt & Schwartz 2001). Small-mammal differentiation on islands has been reviewed by Berry (1996), who argued that the main factor differentiating island races from their mainland ancestors was the chance genetic composition of the founding animals. Conventionally, small populations living on islands are expected to lose genetic variation by drift. Low genetic variability has been reported in Hawaiian monk seals (Monachus schauinslandi) by Kretzmann et al. (1997). Unprecedentedly low levels of genetic variation and inbreeding depression were found by Eldridge et al. (1999) in an island population of the black-footed rock wallaby (Petrogale lateralis) on Barrow Island, Australia. In individually monitored populations of red deer (Cervus elephas) on the island of Rum, inner Hebrides and feral Soay sheep (Ovis aries) on Hirta (St Kilda archipelago, outer Hebrides) two processes have been identiﬁed, however, that mitigate loss of genetic variation. First, population reductions are associated with selection and second, male-biased mortality may lead to changes in reproductive success of young males that broaden genetic representation compared with expectation (Pemberton et al. 1996). More recently, from a study of feral Soay sheep on Hirta, Coltman et al. (1999) suggested that parasite-mediated selection acts to maintain genetic variation in this restricted island population by removing less heterozygous individuals. Genetic variation across populations of a post-glacial colonizing rodent species, Microtus longicaudatus, was consistent with vicariant events (i.e. biota splitting through the development of natural biogeographical barriers) during the Pleistocene and subsequent northern post-glacial expansion following the receding Laurentide and Cirdilleran 495

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ice sheets in the Paciﬁc northwest. Sub-clade differentiation, as between the islands and north coast of southeast Alaska was caused by genetic differentiation during prolonged periods of isolation, possibly as a result of mid-Pleistocene climatic events (Conroy & Cook 2000). Late Middle Pleistocene deposits in the West Sussex coastal plain have preserved discrete high sea-level events, with fossils from these marine sediments including a small form of horse and a distinctive form of northern vole (Microtus oeconomus) (Bates et al. 2000).

The ability of mammals to exploit the intertidal zone and colonise islands Any exploitation of the shore by terrestrial mammals has to be intermittent and cyclical on a tidal basis. Foraging excursions onto the shore by reluctant swimmers are possible at low tide, with retreat being necessary as the tide ﬂoods. No terrestrial mammal is known to retreat into air pockets trapped in crevices or beneath rocky overhangs in the littoral zone; a strategy adopted by small air-breathing arthropods at high tide (e.g. Morton 1954). The height of tidal inundation of coastal habitats clearly varies daily, weekly and seasonally and is inﬂuenced unpredictably by storm surges and ﬂoods. Evictions of terrestrial mammals from feeding or nesting sites in coastal ecotone habitats may follow such events, which can be locally catastrophic. Certain marine mammals, however, like manatees, dugongs and bottle-nosed dolphins, may exploit the littoral zone during its periodic immersion at high tide. Conversely, some marine mammals periodically haul out onto dry land (seals, sea lions), or pursue prey onto shores (bottle-nosed dolphins, Indo-Paciﬁc hump-backed dolphins, killer whales). Some bats may roost in sea caves, and sea caves may be used by seals as pupping sites. Fringing densely wooded country, shorelines and beaches form unobstructed highways for larger animals, as do lake shorelines (Bergerud 1985). Historically, such highways may have played an important role in facilitating the dispersal of large ambulatory animals, from dinosaurs to mammals (Walsh et al. 1992, Allen 1997, but note Coelho 1999), including colonising human “strand lopers”. Linear corridors do, however, have certain disadvantages in acting as magnets for carnivores (cf. James & Stuart-Smith 2000). It is reasonable to suppose that the wind-related noise of surf crashing along exposed coasts (Wilson et al. 1996, Deane 1997, Tkalich & Chan 1998), could be useful in masking the approach of predators to prey with acute powers of hearing (Leuthold 1977). Mammals are sensitive to noise and to ground-transmitted (or ice/water-transmitted; Stirling 1974) vibrations. Thus rabbits communicate effectively by thumping the walls of their burrows. Sea otters are said to have exceptionally ﬁne hearing (Limbaugh 1961). The importance of coastal refugia as important post-glacial recolonization routes has been studied by Byun et al. (1997, 1999) in the Haida Gwaii archipelago (Queen Charlotte Islands, Canada). They analysed cytochrome b sequences (719bp) of black bear (Ursus americanus), one of the distinctive endemics of Haida Gwaii, and compared these with conspeciﬁcs from across North America, focusing primarily on the northwestern coast. The Haida Gwaii bear were indistinguishable from coastal bear of British Columbia and Vancouver Island but were highly distinct from continental bear. Coastal and continental bears differed by 24 synapomorphies and an average sequence divergence of 3.6%. The 496

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coastal mitochondrial lineage occurred in each of the three recognised coastal subspecies suggesting that the morphological characteristics differentiating the taxa may be postglacially derived. The data were consistent with the hypothesis that a glacial refugium existed on the now submerged continental shelf connecting Haida Gwaii, Vancouver Island, and the coastal fringe of mainland British Columbia. This refugium would have been an additional source for post-glacial recolonisation of northwestern North America. A natural marine dispersal ability of small terrestrial mammals, like rats and mice, is by rafting on ﬂotsam (Hedges 1996, Houle 1998) and by swimming for larger species. Transoceanic migrations on ﬂoating “islands” during different geological epochs has been shown to be well within the physiological capabilities (dehydration tolerances of 10–15 days) of small to medium-sized mammals, like South American platyrrhine monkeys and caviomorph rodents (Houle 1998). Hippopotamuses, deer and elephants are the naiads of terrestrial mammals. These are the groups of land animals most notable for swimming to new territory; not all the way to New Zealand, nor to Hawaii or the Galápagos Islands, but certainly to less remote islands. Thus a 30 mile (48 km) sea swim by an elephant has been veriﬁed (Quammen 1996). The popular idea that pigs cannot swim was shown to be mistaken over a century ago by Sir Charles Lyell (1868). In his Principles of Geology he provided evidence (p. 355) of pigs having swum many miles at sea. No less another authority than Alfred Russel Wallace (1913: 301) described seeing a wild pig swimming across the arm of the sea that separates Singapore from the Peninsula of Malacca, “explaining the curious fact, that of all the large mammals of the Indian region, pigs alone extend beyond the Moluccas and as far as New Guinea, although it is somewhat curious that they have not found their way to Australia [that was then]”. The swimming abilities of foxes, muskrat, otters and mink are alluded to below. Seasonal inter-island movements of Peary caribou (Rangifer tarandus pearyi) over sea ice have been veriﬁed between Melville, Prince Patrick and Eglinton Islands in the Queen Elizabeth Islands, Arctic Canada by Miller et al. (1977).

Predation and scavenging by coastal mammals It is true that mammals generally play a minor role in the ecological processes of intertidal environments but their importance can be high at particular times (e.g. seabird breeding season) and their importance as importers and exporters of energy and nutrients across the marine/terrestrial interface has never been established properly (but note Kerley et al. 1996, and cf. Dobrowolski et al. 1993 regarding freshwater lakes). In the opinion of Owen & Black (1990), most predation of waterfowl (70%) was by mammals eating eggs or young. Goose nests are usually in the open and are therefore vulnerable to predators during absences but occupation generally deters most mammals. Duck nests are usually under cover of vegetation and the presence of the female makes little difference to mammalian predators (Owen & Black 1990). The common eider duck behaves energetically like a goose, nesting in the open, and often breeding on islands inaccessible to land predators. It is capable of deterring nearly all avian predators (Owen & Black 1990), but still succumbs to mammalian predators. Adult female eider ducks have a higher mortality rate than adult males because they are at risk of predation by foxes, mink and otters (inter alia) while incubating (Ross & Furness 2000). 497

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Crabs and littoral ﬁshes form the main foci of mammalian predatory foraging on rocky shores, particularly for otters. The crab-eating habit extends to macaques and raccoons (see p. 548), but no information has been tracked down as to whether the crab-eating fox, or zorro (Cerdocyon thous), from South America indulges its habits coastally. The importance of productive shorelines for foraging by grazers, predators and scavengers will be magniﬁed along bleak hinterlands, as witnessed along the Skeleton Coast of Namibia (Tarr et al. 1985) or along the western seaboard of South America abutting the Atacama desert, or the Baja California coast (Ricketts & Calvin 1968, Rose & Polis 1998). Living in such variable and unpredictable environments may be facilitated in small mammals by a capability to enter a torpid state (Bozinovic & Marquet 1991). More predictable bounties of energy coastally come in the form of turtle nests raided for eggs by foxes, bearded pigs, raccoons and mongooses (see below). The consequences of ingestion of raw turtle eggs to humans can be cholera-like diarrhoea (Campos et al. 1996) but their gastric consequences to wildlife seem not to have elicited attention.

Mammal carcasses on shorelines Carcasses of neonate seals may be generated by still-births, starvation, or result from agonistic (or accidental) encounters with adults. Many walruses were crushed to death as a result of panic in a crowded colony caused by human activity (see p. 548). Bodkin & Jameson (1991) studied the pattern of marine mammal (and seabird) carcass deposition along the central California coast from 1980 to 1986. They encountered 194 marine mammal carcasses (mostly California sea lions, sea otters and harbour seals which formed 90% of the mammalian total), with neonates representing 8% of all mammal carcasses. The El Niño–Southern Oscillation (ENSO) proved to be the dominant inﬂuence on interannual variation in carcass deposition. Forty-eight per cent of the marine mammals were washed ashore during the ENSO. Their residence time on the strandline was brief, only 41% of mammalian carcasses remaining after 9 days. A similar pattern was described more recently by Hanni et al. (1997) with respect to strandings of emaciated Guadalupe fur seals along the Californian coast being linked with El Niño events. In different parts of the world (see Fig. 1), such large carcasses might attract scavenging birds (condors, corvids, vultures, giant petrels) and mammals (hyenas, jackals, coyotes, dingoes) that would transfer inland marine-derived energy (and elevated pollutant residues; note Leonzio et al. 1992, Marsili & Focardi 1997), sometimes from quite distant off-shore feeding grounds (Martuscelli et al. 1995, Snyder & Snyder 2000). Birds may even beneﬁt from such bounties at one remove. Thus Jazdzewski & Konopacka (1999) described how ﬁve species of scavenging lysianassoid amphipods constituted 30% of the diet of Antarctic terns (Sterna vittata) captured at King George Island, Antarctica in three consecutive seasons. These off-shore necrophagous amphipods were transported onto the shore with dead seals and penguins, and presumably could be replenished with tidal inundation. In order to compare patterns of decomposition and arthropod invasion of cadavers in intertidal and adjacent terrestrial habitats in the Hawaiian islands, Davis & Goff (2000) used domestic pig carcasses as an animal model. They found that the rate of biomass depletion was slower in two intertidal sites studied (Coconut Island and an anchialine pool on Oahu), and decomposition was primarily due to tide and wave activity and bacterial decomposition. No permanent colonisation of carcasses by insects was seen for the intertidal carcass at 498

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Figure 1 Unidentiﬁed seal carcass stranded at Husvik, South Georgia, Southern Ocean having been picked clean by scavengers (Photo: P. G. Moore).

Coconut Island. In the anchialine pool, dipteran larvae were responsible for biomass removal until the carcass was reduced below the water line and, from that point on, bacterial action was the means of decomposition. Marine and terrestrial scavengers were present at both sites but their impact on decomposition was negligible. These authors recognised ﬁve stages of decomposition at intertidal sites: fresh, buoyant and ﬂoating, deterioration and disintegration, buoyant remains and scattered skeletal fragments. 499

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Coastal mammals and salt Salt is a sought-after commodity by mammals, especially large ungulates (Bennet & Sebastian 1988). Farmers routinely supply salt licks to cattle (Valk & Kogut 1998). Inland, a signiﬁcant mortality factor for caribou in west central Alberta is being knocked down by motor vehicles in winter while licking salt off salt-treated roads (Brown et al. 2000), prompting management by use of roadside salt licks in northern New Hampshire (Miller & Litvaitis 1992). Key deer (Odocoilus virginianus clavium) may be associated with mangrove areas in order to secure a suitable supply of salt and minerals (Klimstra & Dooley 1990). Rabbits are also attracted to wooden pegs impregnated with salt, which they gnaw, and this propensity has been used in Australia to kill them by adding poison to the salt in an attempt to control their numbers (Myers 1975). Notwithstanding this background, the extent to which grazing mammals exploiting tide-washed pastures require, beneﬁt or suffer from elevated salt intake seems not to have attracted much attention. Both Triglochin maritimum and Plantago maritima are salt-tasting and succulent seashore meadow vascular plant species that seem to be attractive to cattle (Jerling & Andersson 1982). Swingle et al. (1996) investigated the growth rates of lambs fed on diets containing halophyte components. Three halophyte forages (Atriplex barclayana, Suaeda esteroa and Salicornia bigelovii straw) were compared. Dry matter intake was higher for lambs fed diets containing halophyte forages than for lambs fed the grass control diet. Because of the increased intake, halophyte-fed lambs were able to gain at the same rate as the controls but feeding efﬁciency was lower and water intake was higher. Carcass merit of all lambs was excellent and not affected by the inclusion of halophyte forages. This work suggested that the form in which salts were present in halophyte forages (which could be important feed resources at moderate inclusion levels) could be important in determining acceptability to animals. The inﬂuence on digestion and mineral balance of inclusion of S. bigelovii biomass (either as stems or spikes of this seawater-irrigated halophyte) in diets for rams has been studied by Abouheif et al. (2000). As the level of stems increased from 0–20% in the diet, nitrogen retention approached maximum. The effect of inclusion in the diet of S. bigelovii on carcass characteristics, minerals, fatty acid and amino acid proﬁles of Najdi camel meat was studied by Al-Owaimer (2000). He found that carcass characteristics were generally not affected by inclusion of 25% Salicornia hay. Feral goats (Capra hircus) were observed by Burke (1990) drinking sea water on the tropical atoll of Aldabra, Seychelles, at the end of extended dry periods when no freshwater was available. Investigation of their kidneys showed no unusual gross morphology. In addition to drinking sea water, the goats produced relatively dry faeces and fed on plants with relatively high water content.

Maritime and intertidal ecosystems Saltmarshes and shore meadows The role of mammals is much greater in saltmarsh, shore meadow and sand-dune ecosystems than in the intertidal zone. Although larger animals are generally believed to have less 500

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impact on tidal wetlands and shallow ecosystems than the more numerous smaller animals, wildlife there can be abundant and ecologically important at certain times and places (Day et al. 1989). Feral Asian water buffalo (Bubalus bubalis) cause considerable damage by trampling among the paperbark tree (Malaleuca quinquenervia) forest in swampy areas with seawater incursion in the Northern Territories, Australia, that are inhabited by saltwater crocodiles (Crocodylus porosus) (S. J. F. Gorzula, pers. comm.). Water buffalo can continue to graze while their noses are submerged by closing their nostrils. Bison (Bison bison) and other ungulates may have browsed Georgia (USA) saltmarshes during the Pleistocene (Edwards & Frey 1977). Since the immigration of European settlers into North America, cattle (Bos taurus) and other domestic ungulates have been grazed intermittently in the salt marshes of the eastern States (Pfeiffer & Wiegert 1981). The composition of current saltmarsh vegetation was considered by Neuhaus (1994) to be mainly the result of long-lasting processes of tidal inundation, grazing, and a permanent inﬂuence of groundwater seepage from surrounding dunes (see also Shaltout et al. 1997). The lower saltmarsh communities in the Ellenbogen and Listland area of Sylt (Schleswig-Holstein, Germany) have changed little over 67 yr due to the effect of heavy grazing (Neuhaus 1994). Maritime pastures that are heavily manured by birds may have an extraordinary resilience to heavy grazing pressure (Williamson & Boyd 1960: 82) and, conversely, many bird species prefer grazed salt marshes (Bakker et al. 1993). Saltmarsh grazing on European marshes, is usually by sheep (Ovis aries) (Ranwell 1972), which crop vegetation closely (Curtis & Skefﬁngton 1998), but also by cows and horses (Equus caballus). This practice seems to be most common along shores from Scotland and Denmark to northwestern France, and is less prevalent in southwestern Europe (Ranwell 1972, Beeftinck 1977). Hares and rabbits are also important grazers of saltmarsh vegetation (Curtis & Skefﬁngton 1998). Georgian salt marshes (USA) are nearly as important as beef producing habitats as the adjacent upland pastures and this use of marshes provides a rationale for protecting them from more damaging uses (Reimold et al. 1975). However, cattle may compete with other wildlife (Lynch et al. 1947). European saltings typically support two to three sheep per acre (0.4 ha) for most of the year when the saltings are free of tides (Ranwell 1972). Spartina (cord grass) and Distichalis marshes support a cow for every 2–4 acres (0.8–1.6 ha) during the 6-month grazing season (Ranwell 1972). In the Red Sea, marshes may be grazed by camels (Kassas 1957). Dominant saltmarsh algae show many adaptations to extreme environmental conditions, one of which is grazing by cattle (Nienhuis 1987). Moderate grazing and consequent treading and manuring of the soil causes algal vegetation to grow in a uniform way. Sensitive algae, like Bostrychia scorpioides and Fucus vesiculosus f. volubilis disappear, as do woody phanerogams and epiphytes, and open spots are formed that may be ﬁlled up with ﬁlamentous green algae. Euryoecious green algae, like Rhizoclonium, Percursaria and Enteromorpha torta are very abundant under these conditions. Heavy grazing and trampling of the soil ﬁnally destroys the vegetation of higher plants and creates a habitat where only algal colonists like Ulothrix species and blue–green algae (now Cyanobacteria) can occur (Nienhuis 1987). Saltmarsh and maritime plant species can persist for long periods in grazed reclaimed land (saltmarsh plants for up to 30 yr or more (Gray 1970) and maritime plants probably for centuries) and may disappear very quickly after cessation of grazing (Gray 1977). The succulent leaves of Aster tripolium are attractive to sheep (Ranwell 1961). Vegetative propagation by detachable axillary buds enables plants to reproduce without ﬂowering but 501

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continued persistent grazing probably accounts for the absence of this species on some Scottish salting pastures, for example, some Argyll marshes (Ranwell 1972). According to Bakker et al. (1993), grazing by cattle in salt marshes in the Netherlands results in both a higher plant species-richness and community diversity than in the absence of management, with hay-making as a management practice having an intermediate effect. Plant species of the lower salt marsh occurred more frequently in grazed than in mown sites of a salt marsh in Schiermonnikoog (The Netherlands), and survival of Aster tripolium and Plantago maritima was higher in grazed than in mown sites (Bakker & De Vries 1992). Artemisia maritima, Halimione (as Atriplex) portulacoides and Plantago maritima are all very common plants in European salt marshes. Plantago maritima, which is the species best adapted to elevated salinity, is markedly affected by sheep herbivory. In a Baltic sea-shore meadow, losses of ﬂowers of this species to grazing were severe and an area of peak abundance of P. maritima coincided with that area less visited by cattle (Jerling & Andersson 1982). Halimione portulacoides showed less response to herbivory and the grazing-deterrent Artemisia maritima was inﬂuenced by herbivory only as a seedling (Dormann et al. 2000). Herbivory causes both an increase and a decrease in plant diversity in saltmarsh habitats. Heavy grazing eliminates sensitive species and produce a dense cover of graminoids in “high marsh” coastal habitats. However, in other marshes, grazing produces bare patches that allows annuals and other “low marsh” species to invade upper marsh zonal communities. Intermediate levels of grazing by sheep, cattle and horses could produce communities with the highest species richness and heterogeneity (Ungar 1998). Mainland salt marshes in Schleswig-Holstein (northern Germany) have been grazed intensively by sheep for several decades. In 1988, experimental plots were established in the lower and middle salt marsh of Sonke-Nissen-Koog and subjected to different grazing intensities. The intensively grazed site (10 sheep ha−1) was covered by a short monotonous Puccinellia maritima sward with Salicornia europaea and Suaeda maritima. Halimione portulacoides and Aster tripolium, especially ﬂowering plants, were rare. On the sites with 1.5 and 3 sheep ha−1, Puccinellia maritima remained dominant. In the absence of grazing, P. maritima was successively replaced by Festuca rubra, Halimione portulacoides and Aster tripolium, and high variability in vegetation height indicated structural diversity. On these bases, Kiehl et al. (1996) advocated cessation of grazing in order to conserve biodiversity. However, in their study of the effects of decreased management on plant–species diversity in a saltmarsh nature reserve in the Dutch Wadden Sea, Esselink et al. (2000), noted that moderate cattle grazing may be effective in maintaining young successional stages suitable for a wider range of halophytic plants, and for breeding redshank (Tringa totanus) and grazing waterfowl. Halimione portulacoides and Limonium vulgare cannot withstand intensive grazing on European salt marshes (Beeftink 1977). Paradoxically, both overgrazing and the absence of grazing can result in a decrease in diversity of species and communities (Westhoff 1971, Beeftink 1977). Beeftink (1977) has summarised recommended grazing pressures for salt marshes (Table 1) and has stressed that stocking densities have to be chosen to accord with conservation of the ecosystem, rather than with the short-term production of domestic animals (this usually amounts to half the normal stocking density). The timing of grazing is important too, because plants can be allowed to set seed if grazed in winter. Breed differences will also affect stocking rates. Many salt marshes in Scotland are unfenced, so stock wander between habitats, usually grazing preferentially on the salt marsh. Stocking rates would also depend on vegetation, which will differ markedly from place to place (e.g. plant communities in northern Scotland are very different from those in England: S. Angus, pers. comm.). 502

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Table 1 Recommended stock grazing pressures (animals ha−1) in various salt-marsh habitats (After Beeftink 1977; see therein for original references). Locality

Stock

Eastern USA Northwest England Europe Southwest Netherlands Southwest Netherlands

Cows Sheep Sheep Sheep Cattle

Salt marsh

2.0 0.3

Brackish marsh

0.6–1.3 Mean 4.5, max. 6.5 5.0–7.5 3.0 0.5

Beach plains

0.5–1.0 0.3

Provision of cattle walkways improves access to marshes (Williams 1955). These are ridges of spoil bulldozed from the marsh and spaced at half mile (0.8 km) intervals and which provide refugia for cattle at high tide (Ranwell 1972). Although higher vertebrates are not important in the energy ﬂow or nutrient cycles of estuaries, estuarine wildlife may be important locally or episodically as consumers, regulators of vegetation and sediment structure, maintainers of habitats, diversiﬁers, food chain “switches”, linkers of ecosystems, and providers of shelter (Day et al. 1989). Animals with high reproductive rates, like the muskrat, respond to the rapidly changing estuarine environment with population pulses. Resources can then be overexploited resulting in substantial population reductions or “eat-outs” of marsh or seagrass vegetation or benthic invertebrates. Overgrazing causes regression phenomena on salt marshes (e.g. increase of Salicornia europaea and S. perennis in Puccinellia maritima stands, and settling of P. maritima in Festuca rubra and Juncus gerardii stands: Beeftink 1977). Fourteen years of protection of coastal lowland vegetation from overgrazing in eastern Saudi Arabia produced great improvements in cover (68%), species richness (33%) and evenness (32%) (Shaltout et al. 1996). On the Gulf and Atlantic coasts of North America, cattle graze the coarse Spartina marsh growths (Ranwell 1972). Interestingly, Ranwell (1967, 1972) noted that Spartina provides suitable fodder for the older breeds of beef cattle and sheep in the British Isles but not for some of the more modern ones. The unpalatability of Spartina to grazers was stressed by Curtis & Skefﬁngton (1998) in their survey of Irish salt marshes (but note Furbish & Albano 1994). Phenolic deterrents in Spartina leaves certainly affect palatability to grazing snails (Barlocher & Newell 1994) and could also inﬂuence food choice by livestock. Floodingtolerant ragwort (Senecio jacobaea) plants, which are hepatotoxic to a number of grazing species, are routinely avoided by grazing cattle in coastal meadows on the Isle of Cumbrae, Scotland, and hence tend to proliferate there (P.G.M., pers. obs.), as also they do in cattlegrazed machair habitats in the Outer Hebrides (Fig. 2). The pyrrolizidine alkaloid, jacobine, present in this plant causes signiﬁcant economic losses due to livestock poisoning, particularly in the Paciﬁc northwest. Some sheep are resistant to poisoning because ovine ruminal biotransformation detoxiﬁes free pyrrolizidine alkaloids in digesta (Wachenheim et al. 1992). Grazing by sheep is an accepted method of controlling young ragwort (Angus 2001) but some ﬂock members seldom eat it. Exposing lambs to ragwort before weaning and grazing newly-weaned lambs with older ragwort-eating sheep after weaning may increase later ragwort eating by lambs (Sutherland et al. 2000). Poisoning of livestock after eating various toxic plants in coastal regions has been widely reported, for example, from Tanzania (Ngomuo 503

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Figure 2 Cow grazing on machair land among untouched ragwort (Senecio jacobaea), Balranald, North Uist, Scotland, August 1993 (Photo: S. Angus).

et al. 1995), Brazil (Gava et al. 1998) and North Africa (El Bahri et al. 2000). The use of grazing animals as weed control agents is established practice; goats especially are capable of controlling spiny or poisonous brush weeds without suffering adverse effects (Popay & Field 1996). Such dietary catholicity is considered to be beneﬁcial or detrimental depending upon circumstances and point of view. Reproductive disorders in sheep have been linked to ingestion of subterranean clovers (e.g. Trifolium subterraneum) containing high concentrations of phytoestrogenic isoﬂavones (formononetin, genistein, biochanin A: Gildersleeve et al. 1991). Such clovers feature in high rainfall areas of coastal southwestern Australia (Bolland et al. 1995). The species richness of vascular plants in four grazed and ﬁve ungrazed shore meadows on the west coast of Finland was studied by Jutila (1997). Vascular plant species richness increased signiﬁcantly with increasing distance from the waterline, and with elevation above mean sea level. It was higher on grazed plots than in ungrazed ones in the delta of the River Kokemaeenjoki but in those transects most exposed to the sea the opposite was true. The inﬂuence of grazing on species richness seemed to be scale-dependent. In a later study, the abundance of annual plus biennial plants, perennials, dicotyledons and pteridophytes was decreased by (cattle) grazing on shore meadows along the Bothnian Sea coast of Finland, while the cover of monocotyledons was increased by grazing. Grazing cattle had various effects but overall it was the stress-tolerant monocotyledonous halophytes that were favoured (Jutila 1999). Experimental work on seed germination after different treatments (controls, watered with brackish water or treated with pesticides) showed that most seedlings were perennial monocotyledons, with Juncus gerardii the most abundant species. The seed bank was signiﬁcantly larger and richer in soil samples taken from the ungrazed, rather than the grazed site. The least number of species and seedlings was produced by the saltwater treatment, and changing brackish water to tap water led to a burst of germination, especially of J. gerardii (Jutila & Erkkilae 1998). 504

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Wetland production in the Rhone delta, southern France, was inﬂuenced by salinity, cattle and horse grazing and water levels. The most productive glasswort marshes were dominated by Arthrocnemum fruticosum, the least productive by A. glaucum. In the nongrazed brackish Phragmites australis – Scirpus maritimus marsh at the river mouth, production was 824 g m2 yr−1, whereas in an unprotected site nearby, grazing was so intense that above-ground vegetation was eliminated (Ibanez et al. 1999). The effects of seasonal ﬂooding and grazing on the vegetation of former riceﬁelds, which had been abandoned for 18 yr, in the Rhone delta were studied by Mesleard et al. (1999). In the absence of artiﬁcial ﬂooding and in the presence of grazing by domestic herbivores (cattle and horses), no signiﬁcant change in plant communities was recorded after 5 yr. The vegetation was mainly composed of halophytes (Salicornia fruticosa and Inula crithmoides). The removal of grazing led to dominance of a salt-tolerant grass, Aeluropus littoralis. Recently, Lafaille et al. (2000) studied the diet of young of the year sea bass (Dicentrarchus labrax) in Mont Saint-Michel bay (France), comparing sheep-grazed and ungrazed tidal marshes. In ungrazed areas, the Atriplex portulacoides community was very productive of litter that was broken down by high densities of the detritivorous talitrid amphipod Orchestia gammarellus. In grazed areas, the vegetation was mainly Puccinellia maritima, a low production grass, yielding a reduced availability of amphipods. Juvenile sea bass browsed over the salt marshes at high spring tides, and fed mainly on Orchestia gammarellus in the ungrazed marshes. This is a ﬁne example of the complex ecological cascade created by mammalian grazing; here extending its impact even to ﬁsh in the water column. Domestic livestock clearly play an important role in salt marshes (Daiber 1982) but the impact of wild mammalian herbivores on saltmarsh vegetation has been less studied (Adam 1990). In the southern USA, muskrats (Ondatra zibethicus) produce “eatouts” similar to those of snow geese (Lynch et al. 1947, Daiber 1982). In 1971, 1.58 million ha of Louisiana coastal marshes produced some 2 million muskrat pelts worth $3 million; the brackish marshes, the best muskrat habitat, produced an average maximum of 16 pelts ha−1 yr−1 (Palmisano 1973). Frequent burning of these areas to facilitate trapping, and with the intention of promoting vegetation development favourable to muskrat, may have been an important factor determining present-day vegetation composition (Hackney & de la Cruz 1981). Fine swimmers, muskrat feed on marsh-fringing vegetation, mostly at night (Edlin 1952).

Sand dunes and machair In contrast to the extensive literature on stock cropping of salt marshes, little information exists about its effects on sand-dune habitats, and the topic has received little experimental study (Ranwell 1972). Populations of sand-living organisms in Israel have decreased due to the stabilisation of the coastal dunes and their massive cover by a few scrub species. The effects of removal of above-ground scrub on annual plants and small mammals has been studied by Kutiel et al. (2000). Small rodents (Mus musculus, Rattus rattus) and the shrew (Crocidura russula) avoided entering the cleared plots, while an endemic sand-living gerbil (Gerbillus andersoni allanbyi) and Tristram’s jird (Meriones tristrami) were well established there. Dune systems in the Prince Edward Island National Park (Canada) harboured a relatively low diversity of small mammals (Silva et al. 2000). The hairy-footed gerbil, Gerbillurus paeba, was the only resident rodent species in the Alexandria coastal duneﬁeld along the eastern Cape, South Africa (Ascaray et al. 1991). Sand dunes in Europe are often 505

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highly affected by rabbit burrowing activities (see p. 519). Overgrazing by reindeer (Rangifer tarandus) has been associated with aeolian sand-dune deﬂation in Finnish Lapland (Seppala 1995). Fore-dune populations of Elymus farctus studied by Harris & Davy (1986) were subject to intense rabbit grazing for much of the year. The increased grazing they observed during the summer of 1979 followed increased tiller densities of E. farctus in the foredunes. However, the often calcareous back-dune machair habitats of the Western Isles of Scotland, which have a relatively high moisture-holding capacity, have supported sheep for centuries without undergoing serious erosion (Ranwell 1972). It is the activity of rabbits that now represents the greatest single problem to machair stability (Angus 1997). That said, the trend in recent years has been to fence-off individual apportionments of machair to conﬁne stock all the year round. This leads to heavy grazing in summer, preventing plants from ﬂowering or setting seed, leading to a reduced variety of species. It also removes cover for nesting and feeding birds and increases the risks of nests being trampled (Scottish Natural Heritage, undated). Cattle actually improve the quality of the machair grassland for other grazers as well as for wildlife. For instance, waders may use hoofprints as nest-cups, while some ringed plovers (Charadrius hiaticula) try to conceal their nest beside a dry cow pat (Scottish Natural Heritage, undated). Machairs closely grazed by sheep are often indicated by a profusion of daisies (Bellis perennis), but sheep will eat young ragwort, which is avoided by cattle (see p. 503) and a pasture heavily grazed only by cattle may be indicated by a high ragwort infestation (Fig. 2). In contrast to machair grassland, machair fens seem to be harmed by grazing, and grazing exclosure experiments on the Monach Isles (Hebrides) showed a dramatic increase in diversity, with a notable increase in orchids when rabbits were excluded (Angus 2001). A cow’s hoof exerts a pressure of 40 lb in−2 to 60 lb in−2 (28– 42 × 103 kg m−2) and an acre of pasture would be trodden some three or four times in a year at normal stocking densities. By contrast, sheep exert much less pressure (25–35 lb in−2 or 17–25 × 103 kg m−2) but tread an acre of pasture six to ten times per year, which is why sheep can be so damaging to dune pastures (Frame 1971). Ponies may graze dunes in concert with sheep without causing untoward damage, cropping the turf almost as closely as rabbits (Ranwell 1972). On Tiree – where there are no rabbits – cattle are grazed on the machair, presumably because the lack of rabbits allows the kind of pasture growth more suited to cattle (Boyd & Boyd 1996). If dry machair is left ungrazed for a period of years, as it often is in burial grounds and airﬁelds, the red fescue grass (Festuca rubra) grows deep and rank and eliminates nearly all other species (Boyd & Boyd 1996). The ﬂoristic richness of the machair depends on the ﬂowering opportunities of the pastures in the face of grazing and cultivation. Orchids, however, are particularly sensitive to grazing, which accounts for their being a spectacular feature of the ungrazed machair on Tiree (Boyd & Boyd 1996). The rare great yellow bumblebee (Bombus distinguendus) has disappeared from some parts of the UK, and is perhaps now an insect equivalent to the rare corncrake (Crex crex) (Angus 2001). It has a number of special requirements, all of which are best met in the Western Isles of Scotland on the Uist machair where there are old burrows of ﬁeld mice (Apodemus sylvaticus) for nest holes, and abundant red clover (Trifolium pratense) on older fallows for foraging (Table 2). It is scarce on land closely grazed by sheep or rabbits, because of the absence of suitable forage species (Edwards 1997). Cattle dung is used for stabilising bare areas in the machair, particularly in the Uists, where it provides a rich seed source, aiding revegetation (Angus 1997). There is an additional problem, however, in that sheep and cattle taking shelter in blowouts, tend to enlarge them 506

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Table 2 Relative frequencies of bumblebee (Bombus) species in different machair habitats in South Uist, Hebrides, Scotland, in relation to the number of years the ground had lain fallow, compared with rabbit- or sheep-grazed areas (Modiﬁed after Hughes 1998 and Angus 2001). Species

1 yr

2 yr

3 yr

Rabbit

Sheep

B. B. B. B. B.

0.73 0.57 0.52 0.31 0.07

0.16 0.69 0.52 0.93 0.29

3.60 0.91 3.40 3.00 4.60

0.52 3.00 0.44 0.82 0.00

0.00 0.23 0.09 0.00 0.07

distinguendus lucorum muscorum jonellus hortorum

(Angus 1997). Shelter from wind is important in reducing susceptibility to infection (e.g. bovine herpes virus: O’Connor 1995). Fore-dune blowouts can also be initiated by anthropogenic trampling (Granja & de Carvalho 2000). The importance of biopedturbation by mammals in deserts has been reviewed by Whitford & Kay (1999) and has resonances for coastal sandy areas. Thus soil ejected from mammal burrows is generally of low bulk density, erodes readily, and varies greatly in concentration of nutrients and organic matter. Increased soil nutrient content characterises warren complexes, especially for central-place foragers and larder hoarders. Foraging pits are relatively short-lived features that trap plant litter and seeds that are rapidly buried. Subsequent germination helps stabilise the soil. The rehabilitation sequence of dune forests after opencast surface mining on the coast of KwaZulu-Natal, South Africa was studied by van Aarde et al. (1996) who reported that species richness for all but the mammalian taxa increased with increasing age of rehabilitating stands. Later mark-recapture studies of small mammal populations did reveal that small mammal communities of young rehabilitating areas converged towards those of older rehabilitating areas. However, year-to-year differences in species composition, especially of older rehabilitating stands, reﬂected the dynamic nature of these communities (Ferreira & van Aarde 1997). Grey (Halichoerus grypus) and harbour seals (Phoca vitulina) frequently haul out among sand dunes (and machair habitats in western Scotland, e.g. grey seals on the Monach Isles) to breed.

Intertidal sand and mud ﬂats Sandy beaches are not typically regarded as mammalian habitats but their importance as highway routes along continents cannot be disregarded. Sand ﬂats and sand bars are important haul-out sites for seals. They allow good visibility of any approaching danger. High levels of vigilance behaviour among harbour seals (P. vitulina) at the periphery of colonies at haulout sites have an anti-predator function (Terhune & Brillant 1996). Interestingly, however, grey seals (Halichoerus grypus) hauled out on beaches have been reported to ignore the alarm calls of other species, for example, mallards (Anas platyrhynchos) (Davies 1949). Coastal red deer (p. 560) will certainly repair to rest on sandy beaches in summer to avoid ﬂy menaces inland. By and large, sand (cf. mud) has good weight-bearing properties. However, in some places sand ﬂats are more treacherous, some containing areas of quicksand, for example, Morecambe Bay, Lancashire, and The Wash, East Anglia, UK. Quicksand may 507

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develop on beaches where pools of water become partially ﬁlled with sand and an underlying layer of stiff clay or other dense material prevents drainage (1999–2000 Britannica.com). The ﬂuidity of sands will vary temporally (tidally and seasonally) according to water content (McMaster 1962). Semi-ﬂuid sand is known to represent a special challenge that has been overcome only by a few invertebrates (e.g. the burrowing ocypodid crab Dotilla myctiroides: Takeda et al. 1996). Large mammals encountering such hazards might well founder because struggling leads to loss of balance and drowning (1999–2000 Britannica.com). Roe deer (Capreolus capreolus), however, have been seen escaping from disturbance by crossing mudﬂats at the head of the Cromarty Firth, Scotland, in places where a human would get stuck (S. Angus, pers. comm.). Frozen beaches can also represent hazards to locomotion for bipeds and quadrupeds alike. When commercial whaling was practised in the Southern Ocean, considerable amounts of blood and oily emulsion were spilled onto beaches and into coastal waters on the island of South Georgia where several permanent whaling stations were located (sufﬁcient to discolour whole bays red; see Hardy 1967, plate 10). In the UK, whale offal was a very serious problem in Harris and Shetland in the early years of the twentieth century when there were four whaling stations in Shetland and one in Harris (S. Angus, pers. comm.). Coastal whaling, on a more limited scale, continues during the annual pilot whale (Globicephala melas) harvest in the Faeroe Islands. Although Hardy (1967) remarked on the impact of this efﬂuent on the phytoplankton and on the bird life adjacent to a ﬂensing plan (a large boarded slipway up onto which whale carcasses were hauled for dismemberment), the impact of this source of organic matter on the littoral zone seems to have gone unremarked. At the abandoned whaling station at Husvik, South Georgia a contour line of methane bubbles along the water’s edge off the ﬂensing plan was observed in 1993. Aggregated along this outgassing contour were large numbers of an unidentiﬁed pink ﬂatworm. This outgassing was presumed to be attributable to decomposition of residual organic matter that seeped for decades through the wooden decking into the ground beneath (Moore, pers. obs.). During the whaling season on South Georgia, the non-utilisable remains of the whales’ carcasses (the skrott) were cast adrift to rot in the harbours. These discards account for some of the very large quantity of whale’s bones deposited around several bays on the island (Headland 1984). These substantial accumulations of weathered whalebone along the strandline (Fig. 3) now form specialist microhabitats for a cryptofauna of mites on South Georgia (Pugh & MacAlister 1994). Whalebone Bay, Bermuda, could be another interesting candidate area for similar research. Lumps of odoriferous ambergris are sometimes found ﬂoating at sea or stranded on beaches in tropical countries (see Beddard 1900 and Pich 1985 for interesting historical details). Sandy beaches are used by turtles and iguanas as egg-laying sites and those nests represent energy bounties for predatory mammals (see p. 533). Ninety-ﬁve per cent of hatchling green iguanas (Iguana iguana) fell prey to raccoons, coatimundis, opossums and other predators in the wild in Panama and, in 1989, they were reported as being extinct on two of the Galápagos Islands and rare on a third (Cohn 1989).

Shingle beaches Although shingle beaches and spits are generally species-poor habitats due to their instability, bound (vegetated) shingle supports some rare plant species, for example, notably 508

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Figure 3 Lichen-encrusted whalebone stranded along the high water mark, Kanin Point, Husvik, South Georgia, Southern Ocean (Photo: P. G. Moore).

the oysterplant Mertensia maritima in Britain (Randall 1977, 1988, 1989). This species is very sensitive to grazing pressure on shingle foreshores. Thus it used to be present only on shingle foreshores of Stockay, the one ungrazed island of the Monach Isles National Nature Reserve, Scotland (Randall 1977). However, it is now absent there (S. Angus, pers. comm.). The decline of M. maritima at a number of sites has been attributed in part to domestic grazing pressure (Randall 1988). Similarly, Lathyrus japonicus decreases markedly in frequency if grazing is allowed (Randall 1977). In general, shingle foreshores are of little economic use for domestic grazing, but this practice is quite common in the west of Scotland and results in considerable changes in species composition, which may also affect habitat mobility (Randall 1977). Human recreational pressure on shingle foreshores causes damage to vegetation by trampling (Fuller & Randall 1988) and disturbance to nesting seabird and wader colonies (e.g. tern species, little ringed plover (Charadrius dubius), Kentish plover (C. alexandrinus) ), the exposed nest sites (note Bullock & Gomersall 1981, Fojt et al. 2000) of which can also be subjected to disturbance problems caused by fox predation (see p. 532).

Rocky shores Rocky shores are probably the intertidal habitat where mammals play the least obvious role. Because most shore ecologists study rocky shores their preoccupation with algae and invertebrates might well account for the lack of attention given to mammals in past shoreline ecological studies. This habitat is mainly the domain of otter species but rodents forage there 509

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too (Navarette & Castilla 1993). Grey seals (Halichoerus grypus) haul out to breed, moult and rest there. The breeding habitat of New Zealand fur seals (Arctocephalus forsteri) on the Banks Peninsula (New Zealand) was characterised variously by large angular boulders, beaches that were steeper than those of non-breeding habitat, and numerous escape zones, crevices and ledges. Non-breeding habitat was less steep, had smaller rounder boulders and was less exposed to the sun (Ryan et al. 1997). Crevices, ledges and slopes at particular sites were particularly useful predictors of breeding status of fur seal populations; information which managers of coastal sanctuaries might ﬁnd useful. Grey seals (Halichoerus grypus) breed colonially on substrata ranging from ice to rocky or sandy beaches. Females prefer breeding close to water (standing pools or sea). Less energetically costly sites (in terms of commuting between pups and water) are likely to be colonised preferentially and by larger, older and more dominant females, potentially generating ﬁne-scale spatial heterogeneity in female quality within a breeding colony (Twiss et al. 2000). New breeding areas occupied during the later phases of population expansion have a higher topographical cost in energy terms than traditional sites (Pomeroy et al. 2000). At low tide, seaweed is sometimes eaten by wild red deer (Cervus elephas), and was also eaten (until their extinction) by Steller’s sea cow (Hydrodamalis gigas), at high tide. Domesticated livestock, notably sheep (Ovis aries), on the Orkney island of North Ronaldsay (and less so feral cattle on Swona), also habitually graze seaweed. Seaweeds are grazed as a last resort by sirenians (when seagrass resources are scarce). In Scotland, red deer and Shetland ponies may be forced in winter to forage at low altitudes by inclement snow conditions on the hill. Each of these is treated in detail below under the appropriate species heading. Surey-Gent & Morris (1987) maintained that many herbivorous animals prefer to graze on seaweed if given the chance, and pointed out that among the “common” names of the egg wrack Ascophyllum nodosum is pig weed, the kelp Alaria esculenta is cow weed, and the red alga Palmaria (= Rhodymenia) palmata is cow or horse weed (at least in Brittany or Norway).

Caves, cliffs and lava tubes Sea caves may be used by seals as pupping sites, although they are potentially hazardous (due to swell action, especially during storms) (Davies 1949, Gazo et al. 2000). Hewer (1974), however, noted that in some parts of southwestern Britain, the ﬁrst sites to be occupied by grey seal cows (Halichoerus grypus) each year were caves. Only later, when the caves were “full”, did seal cows come ashore on open beaches. Hewer (1974) supposed that the attractive feature of caves may not be the cover they represent so much as the deep water approach to the beach itself (cf. Ryan et al. 1997 above). Caves are an important resource, especially where beaches are small and narrow, beneath continuous cliff lines (Anderson et al. 1979). Beach surface area inside the cave may mediate pup mortality in relation to sea conditions (Gazo et al. 2000). The Gress seal cave (near Stornaway, Isle of Lewis) has been described and illustrated by Angus (1997). Sea caves, of course, often evolve into inlet beaches (due to roof collapse) which must have an effect on choice of breeding habitat by grey seals (Davies 1949, Gazo et al. 2000). Figure 4 illustrates a cliff-base cave used by elephant seals (Mirounga leonina), and aromatic Antarctic sealers historically, on South Georgia. Their relentless pursuit by mankind has driven Mediterranean monk seals

510

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Figure 4 Cliff-base sealers’ cave, South Georgia (Southern Ocean). Note centuries of accumulation (to at least 0.5 m depth) of elephant seal dung and moulted hair forming a pillow-like excrescence over the ﬂoor of the cave, worn smooth by the passage of generations of seals (Photo: P. G. Moore).

(Monachus monachus) to the verge of extinction, and caused the last remnants of the population to retreat into coastal caves to breed, many of which have submarine entrances (Mallinson 1978, cf. Angus 1997). Along the high-energy coastline of West Africa, cave collapses cause monk seal mortality (P. Hammond, pers. comm.). Some bats are also recorded from sea caves (see p. 518). Further comment on these aspects is reserved until later. The red-billed chough (Pyrrhocorax pyrrhocorax), an endangered cliff-breeding corvid in the UK, exploits invertebrates (ground beetles, leatherjackets) associated with cow dung deposited on maritime pastures on the island of Islay, Inner Hebrides (McCracken & Foster 1993, 1994). In County Donegal, Ireland, choughs spent longer actively feeding in machair where a greater range of invertebrates were available (Robertson et al. 1995). Recent researches on the Calf of Man (Irish Sea) have revealed how breeding success of the chough is intimately related with sheep grazing and rabbit populations on the island. Declines in chough breeding were linked to declines in rabbit numbers following myxomatosis. Rabbit grazing thus appeared to be as equally important as sheep grazing in the maintenance of the sward characteristics favoured by breeding choughs (McCanch 2000). Cliff edges would become much more hazardous to large mammals (see Fig. 5) at night, or during fogs or storms (especially if thunder and lightning spooked herds living nearby), although cliff-avoidance behaviour is an inherent reaction in rodents (Yoshida et al. 2000). Coastal fogs are seasonally prevalent in certain parts of the world with cliff scenery (Evans 1967) and must compound the hazard. House mice (Mus musculus) live on seabird cliffs, as well as in houses, in the Faeroes (Williamson & Boyd 1960).

511

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Figure 5 Reindeer (Rangifer tarandus) carcass on high shore at base of cliff, Stromness Bay, South Georgia, Southern Ocean (Photo: P. G. Moore).

On volcanic islands, lava tubes form special habitats both for vertebrates and invertebrates. On Fuerteventura (Canary Islands), a volcanic cave called Cueva del Llano (of Pleistocene age), contains pellets derived from Holocene barn owl (Tyto alba) predation, including remains of the now extinct lava mouse (Malpaisomys insularis), whose disappearance was correlated with the appearance of the house mouse (Mus musculus) about 2000 yr BP (Boye et al. 1992, Castillo et al. 2001), possibly carrying a novel ﬂea-borne disease (Boye et al. 1992).

Mangroves Some 116 mammal species have been recorded in association with mangroves in different parts of the world (Fernandes 1997) but there are few mammals which are characteristic of mangrove habitats (note Xeromys under Rodentia, p. 522) (Hogarth 1999). Most species are those that occur in adjacent habitats, although in some cases mangroves may harbour species which have been eliminated elsewhere. Although the presence of some mammals in mangrove forests is restricted to feeding, mangroves are also used as sleeping, roosting and nesting sites, or simply as a suitable area for seeking shelter (Fernandes 1997). Those mammals which do occur in mangroves avoid immersion by being either highly mobile or arboreal. Dugongs and river dolphins are completely aquatic mammals which may be found in mangrove creeks. Otters are also common in South East Asian mangroves, and other smaller carnivores may visit mangroves in search of crabs and ﬁshes, including the endangered ﬁsh cat (Felis viverrima) and mongooses (Herpestes spp.) in Asia (note also Prasad 1992, 512

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Kruuk 1995), bandicoots (Parameles spp. and Isoodon spp.) in Australia, and the racoon (Procyon cancrivorus) in Central America. Present day associations between mammals and mangroves may be interpreted as a result of an evolutionary process by which species have adapted both their behaviour to the inhospitable conditions of the mangrove environment and their gut physiology to the high tannin and salt content of the plants (Fernandes 1997). Terrestrial herbivores, including agouti (Dasyprocta punctata, Agouti paca) and key deer (Odocoileus virginianus clavium) in South America, forage for mangrove seeds and foliage at low tide (Fernandes 1997). In other parts of the world, antelopes, deer, wild pigs and rodents may be common. Plantain squirrels (Calosciurus notatus), rats (Rattus rattus, R. tiomanicus), wild pigs (Sus scrofa), dogs (Canis lupus familiaris) and monkeys (Macaca fascicularis), together with otters and bats are present, if rarely seen, in Singapore mangroves (Ng & Sivasothi 2001). Domestic camels (Camelus dromedarius) and buffalo (Bubalus bubalis) are major eaters of foliage in Arabian mangroves and in the Indus delta of Pakistan (Hogarth 1999). Chital deer (Axis axis) browse direrctly on mangrooves in the Sunderbans region of Bangladesh (Kathiresan & Bingham 2001). The mangrove Avicennia marina, however, is very poor in nitrogen, energy and certain trace elements (such as copper, zinc and manganese) and improved growth of young camels has been reported with appropriate feed supplementation in northern Djibouti (Faye et al. 1992a,b). Monkeys, some of which are omnivorous (see p. 564) and some of which are exclusively herbivorous, are often common in mangroves. Even Bengal tigers (Panthera tigris) frequent mangroves (Hogarth 1999; see p. 529). Capuchin monkeys (Cebus apella) may have a negative effect on mangroves due to the way they disperse the seeds of Phoradendron sp., a parasitic plant found in mangrove forests (Fernandes 1997). They also forage in a destructive manner (Terborgh 1983). This behaviour may be common foraging strategy, that is, one in which depletion of resources occurs in patches in a way that leads to the smallest consequent decline in foraging proﬁtability with time (Fernandes 1997). Bats, whether insectivorous or fruit-eating, and ﬂying foxes (Pteropus spp.) may also be abundant in mangroves, using the trees as roosting sites (Palmer & Woinarski 1999) as well as a source of food. They play a key role as pollinators (Ng & Sivasothi 2001). Thus the cave nectar bat (Eonycteris spelaea) ﬂies long distances to feed on ﬂowers of the mangrove Sonneratia sp. (Berry 1972) and, as a result, it acts as a critical pollinator for this species (Fernandes 1997). The very low diversity of animal pollinators, including Chiroptera, on remote oceanic islands can act as a potent biotic ﬁlter to potential plant immigrants (Elmquist et al. 1992). Large numbers of Australian ﬂying foxes rest in “camps” by day within mangrove forests, dispersing at night. Their main impact is localised destruction of mangrove trees but they do input signiﬁcant amounts of faecal material to the ecosystem (Clough 1982). Fernandes (1997) was correct in concluding that the overall impact of mammals on the ﬂora and fauna of the mangrove ecosystem still remains to be assessed.

Seagrass and kelp beds Seagrass ecosystems in the IndoPaciﬁc and Australia are exploited by dugongs (Heinsohn et al. 1977; and see p. 554), and sea otters (Enhydra lutra) play a key role in the bioeconomics of giant kelp (Macrocystis pyrifera) forests of the Paciﬁc (see p. 541). Strictly speaking, the 513

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sublittoral activities of sea otters fall outside the remit of this review, but it would be remiss not to mention their importance (especially for the useful comparative light it sheds on the impacts of other coastal otter species).

Ice-edge habitats: polynyas, ﬂee edges and land-fast ice In Arctic seas, marine mammals and birds certainly interact at ice interfaces with free water, viz. polynyas, ﬂee edges (Stirling 1997, Gilchrist & Robertson 2000). Interactions between seals and polar bears (Ursus maritimus) on land-fast ice are dealt with under polar bears (p. 546). Surveys of the distribution of ringed seals (Phoca hispida) in the Canadian Arctic archipelago have shown that differences in population density are correlated with the presence or absence of polynyas (Stirling 1997). The pattern of sea-ice ablation inﬂuences when and where polar bears are forced to leave ice for land (Ferguson et al. 1997); something that has consequences for humans as well as wildlife (see below). A relationship between polar bear fractal movement patterns and the fractal dimensions of sea ice was found by Ferguson et al. (1998), indicating a possible mechanism linking geography with polar bear population structure. Subsequently, Ferguson et al. (2000) revealed that polar bears not only followed seasonal changes but anticipated seasonal ﬂuctuations. For example, they were found close to ice edges in spring in advance of the peak availability of edges. During spring and summer, polar bears in the Arctic Archipelago region used land-fast ice more intensively, whereas Bafﬁn Bay bears used moving ice, deﬁned as thick ﬁrst-year ice found in large ﬂees. Both ice types represent areas where most seal pupping occurred in spring in each region. Polar bear population density may not relate directly to prey density, due to the limited ability of bears to track extreme seasonal ﬂuctuations in ice extent found in more productive environments. The Atlantic walrus (Odobenus rosmarus rosmarus) follows the retreat of the ice edge northwards in summer then, as the ice advances in the autumn, moves south again (Mallinson 1978). Floating iceﬂoes provide a means of transportation for seals, walruses and polar bears in arctic regions. However, rafting ice is not necessarily beneﬁcial. Substantial crushing of young harp seals (Phoca groenlandica) (and sometimes adult females) may be due to pinching by rafting ice, after seals had crawled into cracks (Sergeant 1991). Recent satellite tracking has revealed that the feeding grounds of tagged adult female southern elephant seals (Mirounga leonina) are more closely associated with the Antarctic pack ice zone than previously assumed. Some females adjusted their movement patterns to the pulsating sea ice fringe in distant foraging areas while others ranged in closed pack ice of up to 100% cover. Juveniles, on the other hand, avoided sea ice (Bornemann et al. 2000). Both the harp seal (Phoca groenlandica) and the hooded seal (Cystophora cristata) are phocids that give birth, rear offspring and moult on pack ice (Table 3), the distribution of which varies seasonally, interannually, and over longer time periods (Bowen & Siniff cited in Reynolds & Rommel 1999). When pack ice is absent for whelping, harp seals resort to brash and pancake ice, which becomes frozen into a narrow strip along windward coasts (Mansﬁeld 1970). During winter, Antarctic fur seals (Arctocephalus gazella) were signiﬁcantly associated with drift, pancake, brash ice, icebergs and areas of uneven ﬂoe distribution, all characteristic of the marginal ice zone in the southern Scotia and northern Weddell Seas. By contrast, crabeater seals (Lobodon carcinophagus) always inhabit deep pack ice (Ribic et al. 1991). 514

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Table 3 Habitats used by marine seals (Family Phocidae) for breeding and moulting, together with offshore distribution range (Modiﬁed from Bowen & Siniff in Reynolds & Rommel 1999; see therein for original references). Seal species

Habitat

Distribution

Breeding

Moulting

Harp Hooded

Phoca groenlandica Cystophora cristata

Pack ice Pack ice

Pack ice Pack ice

Harbour Grey Bearded

Phoca vitulina Halichoerus grypus Erignathus barbatus

Land, ice Land, ice Pack ice

Land, ice Land Pack ice

Ringed

Phoca hispida

Ribbon Spotted Weddell Ross’ Crabeater Leopard Southern elephant

Phoca fasciata Phoca largha Leptonychotes weddelli Omnatophoca rossi Lobodon carcinophagus Hydrurga leptonyx Mirounga leonina

Fast (lairs) and pack ice Pack ice Pack ice Fast ice, land Pack ice Pack ice, fast ice Pack ice Land, fast ice

Fast & pack ice Pack ice Pack ice Pack ice? Pack ice Pack ice Pack ice Land

Northern elephant Mediterranean monk Hawaiian monk

Mirounga angustirostris Monachus monachus

Land Land (caves)

Land Land

Continental shelf Continental shelf Continental shelf Oceanic Continental shelf Continental shelf Oceanic, continental shelf Oceanic Coastal

Monachus schauinslandi

Land

Land

Oceanic

Continental shelf Continental slope, oceanic Coastal Continental shelf Coastalcontinental. shelf Continental shelf

The mammals Marsupialia Tasmanian devils (Sarcophilus harrisii) use shores as highways and they and other marsupials forage there, mainly for carrion. They also take talitrid amphipods which live there in abundance. Undisturbed sandy beaches in Tasmania usually show the tracks of Bennett’s wallabies (Macropus rufogriseus) and the smaller pademelon (Thylogale billardieri), as well as devils, and smaller species; probably marsupial mice (Antechinus spp.) The last eat talitrid amphipods, both on the forest ﬂoor and probably on the shoreline (A. M. M. Richardson, pers. comm.). The only substantial wild populations of banded (Lagostrophus fasciatus) and rufous (Lagorchestes hirsutus) hare wallabies occur on Bernier and Dorre Islands, off the coast of Western Australia (Short & Turner 1992). Banded hare wallabies occurred mainly in the dunes that form the spine of Dorre Island and on the travertine of its west coast. Rufous hare wallabies burrowed extensively in the inland sand plain and in the dunes on both islands. 515

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Grazing intensities on dune systems on South Stradbroke Island, Queensland by agile wallabies (Macropus agilis) and swamp wallabies (Wallabia bicolor) were negatively associated with the width of the frontal dune and the relative area of open forest vegetation adjacent to the dune (Ramsey & Engeman 1994). Grazing on spinifex grass (Spinifex sericeus) on the foredunes was principally by agile wallabies. Spinifex consumption was related especially to the composition and structure of vegetation in adjacent habitats. Grazing on foredunes by wallabies signiﬁcantly affected the species composition of the foredune community by excluding the establishment of a number of perennial foredune plant species (Ramsey & Wilson 1997).

Insectivora Shrews Yonge (1966) makes passing reference to shrews on shores. Water shrews (Neomys fodiens) have been recorded from rocky shores and salt marshes in Scotland, but so few records exist that their status is difﬁcult to deﬁne. They are found in coastal sites in Kirkcudbrightshire and the inner Solway (Solway Firth Review 1996). Pygmy shrews (Sorex minutus) are recorded from machair habitats in the Western Isles of Scotland (Angus 2001). An ancient coloniser, this is the most ubiquitous British shrew species, being absent from only a few island groups (like Shetland, the Scilly and Channel Islands) (Harrison Matthews 1982). In California, the destruction of nests of the Suisun shrew (Sorex ornatus sinuosus) by tidal inundation has been described by Johnston & Rudd (1957). Shrews reached high population densities along the marsh/grassland ecotone, but the precise locations of social groups seemed unrelated to the presence of particular plant species or to the amphipod (unspeciﬁed) food supply. Sub-adult males of this endangered subspecies wintered mostly outside of social groups in the marsh below high tide level. Potential predators included foxes (probably Vulpes vulpes) and skunks (Mephitis mephitis) (Hays & Lidicker 2000). These authors stressed that conservation efforts need to focus on preserving the tidal marsh ecotone without promoting contact with the upland subspecies.

Hedgehogs Green et al. (1987) conjectured that the punctures that they found in remains of wader egg from clutches destroyed by predators could have been caused by hedgehogs (Erinaceus europaeus) or mustelids. In what appears to be the ﬁrst documented case of hedgehogs threatening internationally important wader populations with regional extinction, Jackson & Green (2000) reported the considerable impact of hedgehog predation on wader populations in machair habitats on the island of South Uist (Outer Hebrides, Scotland). There were signiﬁcant declines in wader numbers and nesting success throughout the island following the spread of introduced hedgehogs. Predation of wader eggs by hedgehogs was frequent, but susceptibility varied between wader species, being between 0–60% of nests. Using

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a combination of mark-resighting and radio-tracking techniques the authors estimated a hedgehog population density of 57 adults km−2. They calculated that wader eggs provided only 0.7–5.5% of the energy requirement of hedgehogs, even during the period when most eggs were taken, and argued that the abundance of hedgehogs and the intensity of their predation on wader eggs was unlikely to diminish in response to wader population declines. They therefore suggested that local extinctions of susceptible wader species were likely if no action was taken to reduce hedgehog predation. The hedgehogs live mainly in rabbit burrows and feed at night, ﬁnding bird nests by blundering into them rather than by actively searching for them (Angus 2001). This problem stemmed from the introduction of seven hedgehogs to South Uist in 1974 in the mistaken belief that they would control garden slugs (see Angus 1993a). By 1999 there were estimated to be 5000 hedgehogs spread over South Uist and Benbecula and moving, even then, into North Uist (Angus 1993a). Surveys on the Uists in 1993 and 1995 revealed alarming declines in the populations of dunlin (Calidris alpina) by 63%, ringed plover (Charadrius hiaticula) by 57%, snipe (Gallinago gallinago) by 43% and redshank (Tringa totanus) by 40%. Lapwings (Vanellus vanellus) showed a more moderate decline and only oystercatcher (Haematopus ostralegus) numbers remained stable, doubtless because a hedgehog cannot get its jaws around their larger eggs. There are no foxes on the Uists, so the problem was not of their making (Broomﬁeld 1999). Attempts to control hedgehogs there are now being made. Figure 6 shows the distribution of hedgehogs in the Western Isles in 1991. Interestingly, ﬁeld experiments with free-ranging hedgehogs have shown a reduction in foraging effort in response to badger (Meles meles) odour over periods of 5–30 min, but no evidence of site avoidance over a 24-h period (Ward et al. 1997). Such limited response to predator odour suggests that it might be difﬁcult to protect vulnerable prey from hedgehog predation by non-lethal means (e.g. by deploying predator scent screens). Non-lethal approaches to predation problems are preferred but are often ineffective or too expensive. It has been shown by Hoover & Conover (2000), using captive coyotes (Canis latrans), that it may be possible to deﬂect predation away from selected nests using pulegone, a volatile irritant chemical. By exposing local predators to pulegone-injected eggs prior to the nesting season and then spraying pulegone on the ground around those nests whose protection was desired, predation may be deterred. The hedgehog story in the Uists has highlighted a deﬁciency in conservation law in the UK. While it is illegal under the Wildlife and Countryside Act (1981) to release into the wild any animal or bird which is not ordinarily resident in Great Britain, there are presently no legal sanctions against inappropriate release of native species into localities where they do not ordinarily occur (Broomﬁeld 1999).

Moles C. L. Grifﬁths (pers. comm.) reports seeing Cape golden moles (Chrysochloris asiatica) in South Africa working over patches of drift kelp, presumably targeting kelp ﬂy larvae or associated Coleoptera. This species is common in the dunes of the Western Cape. Golden moles are fossorial desert-dwelling species (see Fielden et al. 1990), but the Namib desert abuts the coast. Functionally blind, these nocturnal insectivores, forage on the surface at night using seismic cues (Narins et al. 1997).

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Figure 6 Hedgehog (Erinaceus europaeus) distribution in the islands of the Outer Hebrides, Scotland in 1991 (modiﬁed from Angus 1993a).

Chiroptera Few bats are associated with the coast but the population dynamics of the non-succulent plant-visiting lesser long-nosed bat Leptonycteris curasoae, which roosts in a sea cave in Jalisco, Mexico have been studied by Ceballos et al. (1997). The size of the roost varied from c. 5000 individuals in March to c. 75 000 individuals in November. From mitochondrial DNA sequence analysis of animals captured from 13 locations in the USA, Mexico and Venezuela, Wilkinson & Fleming (1996) inferred that this endangered species probably migrates along the Paciﬁc coast of Mexico. The roosts of Nyctophilus bifax in littoral rainforest in Iluka Nature Reserve, on the North coast of New South Wales, Australia have been described by Lunney et al. (1995). Bats roosted 518

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communally in foliage and tree hollows of a variety of tree species. The small tree, Cupaniopsis anacardioides, found in the littoral zone was selected in May. Trees in the forest interior were used at other times of year. The lesser dog-faced fruit bat (Cynopterus brachyotis) and the long-tongued nectar bat (Macroglossus minimus) are mangrove inhabitants in Singapore. The former is the more common, the latter is rare and more restricted to mangroves (Ng & Sivasothi 2001). Macroglossus minimus is a regular visitor to mangroves (Sonneratia alba) when they ﬂower in January and April in Sarawak. These trees only ﬂower at night and are pollinated by these long-tongued nectar bats. This bat also visits ﬂowering durian and other economically important trees (Hazebroek & Abang Kashim bin Abang Morshidi 2001). Fish-eating bats (Pizonyx; presumably P. vivesi) have been reported from the west central region of the Gulf of California. Although they feed on sardines, they were originally thought by collectors to feed on excreta of petrels, since they were only ever collected under loose stones associated with petrels on three tiny offshore islands (Gudger 1943). In a later paper, Gudger (1945) discussed the nocturnal ﬁshing habits of Caribbean ﬁshing bats (Noctilio leporinus) from Ecuador and Monos Island, Trinidad. The stomach contents of this species were full of ﬁsh remains. Fishes were captured in the interfemoral membrane or by the hook-nailed toes of the hind feet, or by both means, as bats swoop over the water. Gudger (1945) speculated that bats may have learned to ﬁsh accidentally, catching ﬁsh at the surface of the water when seeking aquatic insects. Nitrogen concentrations are markedly higher and sodium concentrations marginally higher in faeces of bats that are carnivores and omnivores compared with frugivores (Studier et al. 1994), which will have ecological consequences in places where faeces accumulate.

Lagomorpha Rabbits The animal that has had the greatest visible effect on sand dunes is undoubtedly the rabbit, Oryctolagus cuniculus (Boorman 1977). Rabbits were reported by Atkinson (1949) to “swarm in the sand dunes” of the Monach Isles, western Scotland. Lagomorph faecal pellet abundance was higher on sand dunes than elsewhere in sites studied by Moller et al. (1998) in New Zealand. The loose dry soils of sand dunes provide rabbits with an ideal substratum for their burrows and even in high summer the damp slacks provide some grazing. In medieval times the rabbit was deliberately cultivated in sand dunes, but control was difﬁcult and wild populations became permanently established (Ranwell 1972). Kolb (1991) reported a population density of rabbits in sand-dune habitats in eastern Scotland of 12.1–16.8 ha−1, with burrow counts of 263 (used) and 496 (total) burrows ha−1. Rabbit burrows can become the focus of “blowouts” which can destabilise dunes. The digging activities of rabbits continually rework dune sediments. “Scrapes” also expose and destabilise sand surfaces (Angus 1997). Rabbit grazing pressure tends to be restricted to areas around active burrows. Generally, young rabbits do not show a close association with their original burrow. They have considerable freedom of movement around different burrows, related to the social system of adults in sand-dune habitats (Vitale 1989). On sand dunes in eastern Scotland, very few points were more than 10 m from a burrow entrance, but scared rabbits bolted up to 80 m before entering holes. Burrow ﬁdelity varied considerably between individuals and activity depended on season, age and sex (Kolb 1991). Rabbits have even been recorded burrowing 519

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below High Water Mark (HWM) in North Uist (Beveridge 1934), and elsewhere in the western isles of Scotland (S. Angus, pers. comm.). Adult rabbits suffered lower mortality than juveniles in sand-dune habitats, with no signiﬁcant differences in condition between males and females. Only in very severe winters did they starve, when juveniles suffered more than adults (Wallage-Drees 1986). The extent of their damage to dune habitats will obviously depend on rabbit population density. Rutin (1992) described the effects of rabbit digging activities on the erosion rate and stability of sandy slopes in a Dutch sand-dune system at De Blink. He distinguished two types of activity: building of caves and sand mounds of <1.5 m2 in area, and digging of shallow burrows, whereby amounts of sand <1 kg per burrow were excavated. Burrowing activity was found over the whole dune, but cave holes were dug mainly on the northern slope. Shelduck (Tadorna tadorna) beneﬁt from rabbit burrowing activities in sand dunes by nesting in rabbit burrows (Peterson et al. 1972) but this accommodation may be even more important for rats, mice and even hedgehogs (Angus 2001). Atkinson (1949) noted that on Eilean Mor (Flannan Isles, northwestern Scotland) “below ground, pufﬁns and rabbits were hopelessly mixed up, and fork-tails (Leach’s fork-tailed petrel, Oceanodroma leucorrhoa) sometimes had their own private nesting chambers in the general labyrinth”. This situation remains current (S. Angus, pers. comm.). In New Zealand, predation of yellow-eyed penguin (Megadyptes antipodes) chicks may be reduced by removing stock around penguin breeding sites, because long grass may reduce lagomorph abundance and hence small mammal predators (Moller et al. 1998). The structure of sand-dune communities in Europe prior to myxomatosis was effectively the product of intensive rabbit grazing (Ranwell 1972). A low density population helps maintain vegetation diversity by keeping vigorous species in check. Competitionintolerant annuals may be conﬁned to the vicinity of burrows. Higher densities of rabbits close crop the sward to a 2 cm-high carpet. At even higher rabbit densities this sward breaks down and areas of bare sand result. Once this stage has been reached, recovery is slow. Sand-dune mobility may then become a problem (sometimes even swamping offshore ecosystems, like oyster beds; see Landry et al. 1996). The advent of myxomatosis in the 1950s caused a widespread decline in rabbit numbers. This release of grazing pressure resulted in the expansion of scrub vegetation in many dune systems. White (1961) records much improved growth and ﬂowering of Elytrigia juncea (as Agropyron junceiforme) in embryo dunes following loss of rabbits from myxomatosis. Since then, however, rabbit numbers have increased again to such an extent that overgrazing is once more an issue in certain areas (Boorman 1977). In sand-dune systems, where a herbaceous layer is rare, rabbits eat the stems, leaves and fruits of shrubs (Rogers et al. 1994 and references therein). Analysis of rabbit faecal pellets on Ynyslas dunes, North Wales, revealed that Festuca rubra, Poa sp., Holcus lanatus and Ammophila arenaria formed over half the diet. Festuca rubra was the most important species. In a “cafeteria” experiment, F. rubra was preferred, closely followed by Poa trivialis; Agrostis capillaris and Anthoxanthum odoratum were intermediate, with Lolium perenne apparently unpalatable. At Ynyslas, Elytrigia juncea suffers far more severely from rabbit grazing than Ammophila (which is more at risk from erosion following burrowing); selective grazing severely restricts its seed production (Packham & Willis 1997). Bourel et al. (1999) examined necrophilous insect succession on rabbit carrion in sanddune habitats in northern France. They reported that 66 arthropod species were involved with the decomposition of the six freshly killed carcasses deployed. 520

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In machair in the Hebrides, rabbit grazing is a particular problem in late summer and autumn, when plants can be cropped before they have had a chance to set seed, eventually reducing the number of species – especially of annual plants – and encouraging the spread of mosses (Angus 1997). In the Camargue, the choice of plants eaten depends largely on the balance between salt and water in the available standing crops. For example, the reason that rabbits eat apparently unappetizing grasses (dry Bromus, Agropyron) in summer may be that Atriplex and Halimione contain too much salt (Rogers et al. 1994). Rabbits have been introduced to 48 islands off the Australian coast, and where no predators and competitors are present have often completely destroyed or transformed the vegetative cover. The vegetation changes and heavy erosion that have followed the introduction of rabbits onto coastal islands have posed serious threats to the existence of seabirds which use the islands as resting and breeding sites (Myers et al. 1994). Direct effects of rabbits on tern colonies have been reported elsewhere (Brown 1974, Courtney 1979). The background to the distribution of rabbits on South African offshore islands has been detailed by Cooper & Brooke (1982). Reduction of rabbit grazing pressure by control measures resulted in more luxuriant growth of vascular plants on subantarctic Macquarie Island (Copson & Whinam 1998). Feral white rabbits eat marine algae on Schaapen Island, Saldanha Bay, South Africa, depending on the algae to survive the dry summers (C. L. Grifﬁths, pers. comm.).

Hares Brown hares (Lepus europaeus) may graze at lower zones on salt marshes than rabbits (Adam 1990, Solway Firth Review 1996) and have been seen, on occasion, on mudﬂats (Adam 1990), as occasionally, admittedly, have rabbits (Cadman 1974). Brown hares have been shown to facilitate grazing by brent geese (Branta bernicla) in a temperate salt marsh in the Netherlands by retarding vegetation succession for >25 yr (sheep grazing has similar positive effects for brent geese foraging on salt marshes; see Gray & Scott 1977). Winter grazing by hares prevented the shrub Halimione (as Atriplex) portulacoides from spreading to the younger parts of the salt marsh (van der Wal et al. 2000). Their grazing contributed to the low abundance of H. portulacoides in the early and mid-phase of saltmarsh succession in a Dutch salt marsh (Dormann & Bakker 2000). A reduction of at least 44% in the carrying capacity of the marsh for brent geese was calculated to be the consequence of an absence of hares (van der Wal et al. 2000). The diet of the Irish hare Lepus timidus hibernicus was investigated in several different habitats in western Ireland by Tangney et al. (1995), including machair. Forbs (i.e. pasture herbs), made an important contribution to the diet on machair, the study area where they were most plentiful. Shrubs (like Thymus polytrichus (as praecox) ) were an ancillary food source on machair, especially in winter when they provided food when otherwise live tissue was scarce. Hares are found on very few offshore islands (none in New Zealand for instance, compared with over 40 islands with rabbits) and would be classed as very poor colonisers. Yet in the Netherlands, where hares are highly regarded as game, they have been introduced to many islands, as have rabbits, with almost exactly the same results. They very seldom coexist on islands of <1000 ha but, unexpectedly, hares (given their larger size, lower population density and far larger home ranges) are found on the two smallest Dutch islands on which lagomorphs still live (Flux 1994). Birks & Dunstone (1984) noted that hares were especially common at sites they studied along the Galloway coast, southwest Scotland, and that they were often seen amongst rocks on the shore. 521

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Rodentia According to Woinarski (2000), the three pervasive processes that should be given priority in management of the rodent community in the Northern Territories, Australia, and that have the greatest impacts are: ﬁre, grazing and feral predators (cf. Lunny 1987). Patterns of habitat use by the rodents Pseudomys novaehollandiae and Mus domesticus were investigated on a 6.8-ha area of coastal heathland almost 5 yr after it last burned in the Myall Lakes National Park, Australia by Haering & Fox (1997). They found that immature Pseudomys novaehollandiae were found mostly in short dense wet heath and may have been dispersing. Both species are spatial generalists, but Mus domesticus acts as a fugitive species occupying niches left vacant by Pseudomys novaehollandiae and other small mammal species coexisting in the study area. Fire might be expected to have less impact on fast-moving mammals than on slow-moving coastal vertebrates like tortoises (see Hailey 2000).

Rats Yonge (1966) noted that brown rats are common about high tide level where they feed on the refuse of the strandline (note also Nicol 1936, Gibson & Shillaker 1974). On the subantarctic island of South Georgia, inadvertently introduced brown rats (Rattus norvegicus) now reside among, and burrow into, the tussac grass (Parodiochloa ﬂabellata) stools which fringe the shoreline along raised beaches. Tussac is rich in carbohydrates, especially the leaf bases, and provides a major part of the rats’ diet throughout the year (Headland 1984, Strange 1992). The tussac stools are ideal habitats in which rats can excavate nests, which they line with dry litter, insulating them very efﬁciently (Headland 1984). Rats were probably introduced to South Georgia around 1800, allegedly at Prince Olav Harbour (Berry et al. 1979). Buckley & Buckley (1980) noted that those roseate terns (Sterna dougallii) nesting in salt marshes at Shinnecock and Moriches Bays (Long Island, USA) which had placed their nests atop Spartina alterniﬂora wrack or tunnelled into tall Spartina grasses were the least productive because of rat predation and spring-tide ﬂooding. The species that comes closest among mammals to being a mangrove specialist is the rare Australian rodent, the small grey false water-rat Xeromys myoides. This small rat forages for crabs (Magnusson et al. 1976) at low tide among Avicennia and Rhizophora, and builds a nest of leaves and mud among the buttress roots of Bruguira above the level of neap high tides. Although its fur is water repellent, it has not been seen to swim, and it probably avoids high tides by climbing trees (Hutchings & Saenger 1987). A sizeable population of breeding pufﬁns (Fratercula arctica) is thought to have been destroyed by ship rats on Lundy Island, Bristol Channel (Flegg 1972; Smith et al. 1993). The same pattern of rat colonisation of islands has been repeated elsewhere in the UK, for example, on the Calf of Man, the Shiants and Ailsa Craig (Williamson & Boyd 1960) and elsewhere, like on Ascension Island (Pain et al. 2000). Brown rats landed on the island of Ailsa Craig in the Firth of Clyde, Scotland in 1889, off ships. By 1984 they had eradicated the entire pufﬁn population (Haswell-Smith, 2000). After their successful eradication by 1991, fulmars (Fulmarus glacialis) showed immediate improvements in breeding success. The largest Clyde fulmar colony is presently on Sanda Island where rats are absent (Zonfrillo 2001). Rats are a threat to hole-nesting and colonial seabirds (Gibson-Hill 1947, LeCorre & Jouventin 1997, Drever & Harestad 1998, McChesney & Tershy 1998, Hobson et al. 1999, 522

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Howald et al. 1999, Zino et al. 2000), except perhaps on islands to which they have never gained access, or on islands from which they can be eliminated by poisoning, although the latter is very difﬁcult to achieve efﬁciently (Taylor & Thomas 1989, 1993, Furness & Monaghan 1987, Taylor et al. 2000). Inadvertent secondary poisoning of avian scavengers (corvids, bald eagles) has been reported during attempts to control rats with the anticoagulant brodifacoum (superwarfarin) in seabird colonies in British Columbia (Howald et al. 1999, Taylor et al. 2000). In July 1995, Frégate Island in the Seychelles was colonised by Rattus norvegicus and an eradication campaign was mounted in June 1996 to protect the unique fauna of the island, not least the critically endangered Seychelles magpie-robin (Copsychus sechellarum). The attempt was abandoned, however, after several magpierobins died through secondary rodenticide poisoning (Thorsen et al. 2000). Experiences with rat extirpation campaigns on offshore islands in New Zealand are related by Ogilvie et al. (1997) and Dowding et al. (1999). The successful removal of rats (Rattus norvegicus and R. exulans) from Kapiti Island, New Zealand resulted in signiﬁcantly improved survival rate for stichbirds (Notiomystis cincta) and saddlebacks (Philesturnus carunculatus) (Empson & Miskelly 1999). Signiﬁcant contamination of soil or water seems unlikely after only a single application of poison (Ogilvie et al. 1997). Land crabs (Gecarcinus lagostoma) seemed unaffected by this rodenticide on Ascension Island (Pain et al. 2000; but note Ogilvie at al. 1997 on freshwater crustaceans). Any such risks to non-target species need to be weighed against the beneﬁts of rat removal programmes (Short & Turner 1993, Myers et al. 1994). With careful planning and adequate resources, however, rodents can be eradicated from small (Taylor & Thomas 1989, Haswell-Smith 2000), and even quite large, islands (Taylor & Thomas 1993, Empson & Miskelly 1999). Aerial baiting allows efﬁcient and cost-effective coverage of wide areas (Thomson 1986, Dowding et al. 1999, Priddel et al. 2000). Strange (1992) noted that rats on the Falkland Islands ate not only the growing points of tussac grass but also ground-nesting birds, like petrels, and their eggs. He noted that islands with populations of brown rats were noticeably clear of smaller, ground-nesting species. Imber (1975) suggested that petrels are endangered by rats only if their body weight is less than that of the mammal, and this may apply to other seabird groups (Furness & Monaghan 1987). Since the 1950s, some large seabird colonies on the Queen Charlotte Islands archipelago in British Columbia have undergone major declines. Predation from introduced rats (Rattus spp.) has contributed to declines in burrow-nesting seabirds there, especially ancient murrelets (Synthliboramphus antiquus) (Bertram & Nagorsen 1995, Drever & Harestad 1998, Howald et al. 1999), which by 1993 had been reduced to 10% of its historical population size (Howald et al. 1999). Norway rats (Rattus norvegicus) reduced the breeding success of grey-faced petrels (Pterodroma macoptera gouldi) by 10–35%, by eating unattended eggs and killing young or weak chicks on Whale Island (Moutohora), Bay of Plenty, New Zealand (Imber et al. 2000). This species has recently replaced the black rat (Rattus rattus) (see Bertram & Nagorsen 1995) on Langara Island, at the northwestern tip of the Queen Charlotte Islands archipelago. Their diet was investigated by Drever & Harestad (1998) to evaluate their signiﬁcance as predators of breeding ancient murrelets. They found that items occurring in high percentage volume (>50%) included plant shoots, berries, amphipods and tissues of ancient murrelets. The occurrence and volume of each food type varied positively with their apparent availability on the island. Rats near the coast fed primarily on marine invertebrates, fruits and seeds, whereas rats in the interior fed primarily on terrestrial invertebrates and plant shoots. Those trapped within the murrelet colony had the highest frequency of murrelet remains in their stomach contents. Stable isotope analysis 523

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(stable carbon (δ13C), nitrogen (δ15N) especially, and sulphur (δ34S) less so) has recently been used to assess accurately the importance of seabirds in the diets of rats that consume a variety of plants and invertebrates (Hobson et al. 1999). The endemic Maori rat Rattus exulans is a small rat, much gentler in habits and less aggressive than either R. rattus or R. norvegicus. It has been found in New Zealand that R. exulans does not do harm in bird island sanctuaries. On many of the islands of the Paciﬁc, the native R. exulans was exterminated either by cats or by the arrival of larger Rattus species (Elton 1958). The endemic rats were once highly prized by natives both for food and sport (Elton 1958). Two endemic rice rat species (Nesoryzomys fernandinae and N. swarthi) described by Charles Darwin, and since thought to have become extinct, have recently been rediscovered in the Galápagos Islands (Dowler et al. 2000). From an analysis of marks made on wax replicas of limpets by predators, notably crabs, Thompson et al. (2000) reported ﬁnding marks that matched the bite marks of the rat (R. norvegicus) seen occasionally foraging on the rocky shore. Predation by R. norvegicus in the rocky intertidal in Chile has been studied by Navarette & Castilla (1993). Of the ﬁve mammal species (Rattus norvegicus, Akodon olivaceus, Oryzomys longicaudatus, Mus musculus, Marmosa elegans) found in the littoral zone of the Las Cruces Reserve, central Chile, Rattus norvegicus was the most abundant. Ten burrows of R. norvegicus contained remains of a total of 40 species of intertidal organisms. The most numerous were keyhole limpets (Fissurella crassa), followed by porcellanid and cancrid crabs. The composition of prey remains in burrows varied greatly, suggesting some degree of specialisation by individual rats. Rats removed the smaller, less abundant sizes of keyhole limpets from the population, probably as a result of mechanical restrictions on dislodging larger individuals. Some of the rats’ prey species were to be found almost exclusively in the holdfasts of the kelp Lessonia nigrescens from the very low intertidal zone. Table 4 shows prey items recovered from stomach samples taken from rocky-shore foraging Chilean rats. In removing elements of the population of this key grazer within the reserve, rats may “substitute” for Table 4 Prey items found in the stomachs (n = 29) of Rattus norvegicus foraging intertidally at Las Cruces, Chile (February 1988–March 1989). Frequency is the proportion of individuals in which a particular item was found. Relative percentage is the proportion of a prey item in the diet (pooling all individuals). Total prey items examined = 73. (After Navarette & Castilla 1993). Item

Frequency

Relative %

Decapoda Polychaeta Gastropoda Littorina peruviana Alpheus sp. Rhodophyta Insecta Woody plant tissue Green plant tissue Seeds Spiders Grit Other

0.45 0.11 0.28 0.10 0.06 0.03 0.48 0.31 0.31 0.14 0.07 0.07 0.07

17.8 5.5 12.3 4.1 1.4 1.4 19.2 12.3 12.3 5.5 2.7 2.7 2.7

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human depletion of limpets (collected for food) outside the reserve. Since rats are less abundant in places distant from human habitation, the proximity of humans can be an important factor in deciding the location of marine reserves, for both obvious and less obvious reasons (Navarette & Castilla 1993).

Mice The greatest cause of egg loss for rhinoceros auklets (Cerorhina monocerata) at the seabird colony on Triangle Island, British Columbia in 1998 was predation by Keen’s mice (Peromyscus keeni) (Blight et al. 1999). Despite studies suggesting that gape-limited rodents are unable to open large eggs, mouse predation was likely to be responsible for the loss of more eggs than all other causes combined, with mice commonly opening and eating eggs of nearly twice their mass. The high mouse predation rate found (34%) was related to temporary egg neglect by foraging parents. Thus egg depredation can be expected to increase in years of low marine productivity when adults are forced to spend more time foraging at sea. Using stable isotopes, Drever et al. (2000) studied the impact of predation by this species on seabird eggs at Triangle Island, which is western Canada’s largest seabird colony. They found that isotope values in the liver and muscle tissues of P. keeni indicated that they preyed primarily on seabird eggs and terrestrial invertebrates. Mice appeared to feed on seabird eggs once they became available and continued to prey on sea birds, most likely in the form of abandoned eggs or carcasses of chicks and adults, throughout the breeding season. High tides in Georgia, USA salt marshes may inundate nests of the marsh wren (Telatodytes palustris griseus), drowning nestlings. Such tides may also increase predation on wren nestlings by the rice rat (Oryzomys palustris), the only rodent permanently resident in Georgia Spartina marshes. Normally the rice rat spends much of its time scavenging nocturnally on the surface of the marsh and feeding on crabs (Sesarma, Uca) and other marsh invertebrates (Pfeiffer & Wiegert 1981). When the spring tides inundate the marsh, Oryzomys climbs up the stems of Spartina, and so is more likely to encounter the nests of the marsh wren. The rice rat’s preference for regions of long grass (Sharp 1967) is thus clearly adaptive. As well as eating the young wrens, Oryzomys will also take over the nest and rear its own young (Kale 1965 cited by Green 1968). Willock (1958) reported ﬁeld (or wood) mice (Apodemus sylvaticus) climbing up into a rose bush to avoid tidal inundation of an East Anglian salt marsh. The saltmarsh harvest mouse (Reithrodontomys raviventris) was reported occasionally from a saltmarsh/grassland ecotone in California by Hays & Lidicker (2000). Corp et al. (1997) studied ranging behaviour and time budgets for adult male wood mice (Apodemus sylvaticus) in Scottish maritime sand-dune habitats compared with deciduous woodland. They found that wood mice in sand dunes exploited ranges 28 times greater than their woodland counterparts. The comparatively lower availability of food on the sand dunes was considered to be the main factor explaining the greater range area, total distance moved, speed travelled and level of activity of mice at this site. Coleopterans formed the bulk of the diet throughout the year (Zubaid & Gorman 1991). Using doubly labelled water, Corp et al. (1999) found that the rates of water inﬂux and efﬂux were signiﬁcantly greater for wood mice living in sand dunes than for animals in woodland. They attributed this to the differences in the water content of their diets (seeds in woodland cf. invertebrates on sand dunes). Sand-dune mice had lower body masses than woodland mice. The diet of feral house mice (M. musculus) inhabiting a sand-dune ecosystem near Dunedin, New Zealand (South Island) was determined by Miller & Webb (2001). They found that 525

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mice were omnivorous, although their diet was biased towards invertebrates. Mouse diet varied seasonally, with lepidopteran and coleopteran larvae being eaten signiﬁcantly more often in summer. House mice living insulated in coastal tussac grass on a remote headland on South Georgia (Southern Ocean), where the mean temperature is <2°C and the monthly average below 0°C for four months of the year, were collected by Berry et al. (1979) for study of their evolutionary genetics. Theirs was the ﬁrst report of house mice living within the conﬁnes of the Antarctic Convergence. The area where the mice occurred (the south coast of Queen Maud Bay; presumably brought there inadvertently by sealers early in the nineteenth century) is part of the Nuñez Peninsula, which is cut off from the main part of the island by branches of the Esmark glacier. This isolation had probably protected them from rats (see p. 522) and likely extinction.

Voles Short-tailed voles (Microtus agrestis) include machair habitats within their range but do not seem to demonstrate a preference for it (Angus 2001). D. Morritt (pers. comm.) reports being made aware of the healthy population of voles (?M. agrestis) on a salt marsh near Bristol (UK) as evidenced by several swimming around after displacement during one particular astronomical high tide. Water voles, Arvicola terrestris, have been found living fossorially on several small turf-covered islands in the Sound of Jura and Loch Fyne, Scotland (R. Green, pers. comm., based on data from C. Craik & X. Lambin). If the reduction in numbers in this, the fastest-declining mammal species in the UK, continues on the mainland then these offshore island populations could become nationally important in terms of conservation. There is some evidence (from both genetic and distributional data) that some dispersal between these islands may occur, despite the notoriously strong currents in the Sound of Jura. The mainland decline of A. terrestris is due both to predation by mink and habitat loss (Telfer et al. in press). The California vole (Microtus californicus) was reported occasionally from saltmarsh and grassland ecotone habitats in Suisun Bay, California by Hays & Lidicker (2000).

Lemmings Lemmings are small cricedit rodents noted for regular population ﬂuctuations. Their population “explosions” occur 3 or 4 yr apart, for reasons that are not completely understood. After several years of optimal breeding conditions and low mortality from predation, the lemmings in an area move in detectable waves away from centres of lemming population. The emigrating lemmings begin to move in greater numbers, at ﬁrst erratically and under cover of darkness, and later in bold groups that may travel in daylight. The movements of the Norway lemming (Lemmus lemmus) are the most dramatic, as many of the migrants may die by drowning in the sea (Edlin 1952). They do not (1999–2000 Britannica.com), as is popularly supposed, plunge off cliffs into the sea in a deliberate, suicidal death march. The potential for scavenging mammals to exploit such occasional coastal bounties opportunistically must arise (Fig. 5), but no literature has been traced on the subject. Similar sudden bounties will occasionally exist under seabird cliffs, as when thick-billed murre chicks (Uria lomvia) launch themselves off ledges into the sea on ﬂedging; as many as 20% to oblivion (Gilchrist & Gaston 1997). Predator avoidance at a time of mass vulnerability 526

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doubtless accounts for the fact that young pufﬁns, when ready to ﬂy, go down to the sea only under cover of darkness (Atkinson 1949). Mallory & Boots (1983) studied the distribution of “mats” made by the collared lemming (Dicrostonyx groenlandicus) on exposed beach ridges on Bathurst and Devon Islands, North West Territories in the Canadian High Arctic. Since vegetation was sparse in this region, mats were easy to identify. Mats were relatively permanent structures with a mean diameter of 3.88 m and an average of 13 burrows. The average mat polygon area (interpreted as territory or home range) was 0.34 ha. The distribution of mats over the environment was non-random, supporting the notion that mats represented core areas within territories that were the result of social interactions.

Porcupines Porcupines (unspeciﬁed) have been ﬁlmed grazing on intertidal seaweeds on the West coast of South Africa (C. L. Grifﬁths, pers. comm.).

Cetacea Cetaceans come into contact with shores both deliberately and inadvertently. Strandings of whales, dolphins and porpoises are not unusual (Bodkin & Jameson 1991). Mostly they involve single live or dead animals (Haug & Gulliksen 1981) but on dramatic occasions live mass strandings occur (Martin et al. 1987, Gales 1992, Lucas & Hooker 2000). Such live strandings are invariably of odontocetes (toothed whales) rather than ﬁlter-feeding mysticetes and more often oceanic than inshore species. The long-ﬁnned pilot whale (Globicephala melaena) is one of the cetaceans most commonly represented in mass strandings, the tight social cohesion of pods being an important factor in explaining their tendency to massstrand (Martin et al. 1987). Mignucci-Giannoni et al. (2000b) associated the live mass stranding of pygmy killer whales (Feresa attenuata) in the British Virgin Islands with oceanographic disturbance due to hurricane Marilyn. It seems likely that individual strandings represent sick, malnourished (Thompson et al. 1990, Diguardo et al. 1995, Jauniaux et al. 1998, 2000), injured or disorientated animals (Chatto & Warneke 2000). Those that have died may have done so as a result of epizootics, shooting (cf. Mawson & Coughran 1999) or other interactions with ﬁshers and ﬁshing gear (Goldstein et al. 1999), or predators like sharks, polar bears or killer whales (Wells, Boness & Rathbun in Reynolds & Rommel 1999), or because of starvation (Kuiken et al. 1994, Berrow & Rogan 1997). These are likely causes if data show no overall seasonal trend (Berrow & Rogan 1997). The reasons behind mass stranding events are obscure, but 93% of G. malaena mass strandings in the Atlantic between 1982 and 1993, were morbillivirus seropositive (Barrett et al. 1995; as G. melas). Mass strandings sometimes make up only one segment of a population; viz. all males in some recorded strandings of sperm whales (Physeter macrocephalus) and Atlantic white-sided dolphins (Lagenorhynchus acutus) (Lucas & Hooker 2000). Adult male sperm whales are known to associate in temporary bachelor schools (Chatto & Warneke 2000). Between 1985 and 1996 neonates made up 30% of all ﬁnless porpoise strandings in Hong Kong. The causes of neonate mortality were unclear but may have involved transference of lipid-soluble organochlorines from mothers to offspring via their milk (Parsons 1998). 527

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Recent theories that mass strandings might relate to geomagnetic topography were not supported by the observations made by Brabyn (1994) in New Zealand although alligators in Florida do seem to possess magnetic sensitivity (Rodda 1983), so such ideas are not outrageous. Where such strandings occur in the vicinity of human populations, strenuous efforts are usually made to reﬂoat animals if they are still alive, or to rapidly remove (bury, incinerate) the carcass if the animal is dead. Because strandings in remote areas may only be detectable from the air, and because carcasses decompose quickly in the tropics, the phenomenon is probably underestimated (Chatto & Warneke 2000). Conversely, enhanced public awareness of cetacean issues in recent years may, in some places, have resulted in increased reporting of strandings (Parsons 1998). Possibly the most bizarre whale stranding is that of a bowhead whale (Balaena mysticetus) that melted out from remnant glacier ice in the lateral moraine of the Jemelianovbreen glacier (Svalbard). This 1500–1600 year-old (carbon dated) abdominally-holed individual, partly ﬁlled with well sorted gravelly beach sediments, had been incorporated into the glacier during an earlier period of glacial advance during the Little Ice Age, to reappear again during present period of glacial retreat (Lonne & Fuglei 1997). During a mass mortality of Mediterranean striped dolphins (Stenella coeruleoalba), 82 individuals were stranded on the Spanish coast. Unusually, epizoitic pedunculate barnacles (Lepas pectinata and L. cf. hillii, Conchoderma virginatum) were found attached to the teeth (Aznar et al. 1994). These authors thought that the unusual epizoite densities (especially of the barnacle Xenobalanus globicipitis on the skin) may have been due to reduced movement of the dolphins and/or impaired immune functioning of the skin prior to death. This malfunctioning might account for the exceptional occurrence of Lepas and Conchoderma on the teeth. The presence of high epizoitic and parasitic loadings on stranded carcasses may be indicative of poor bodily condition (Bonnell & Dailey 1993). Very often, stranded cetaceans are rather emaciated (note Jauniaux et al. 1998, 2000, Dailey et al. 2000). Fungal opportunists, like Fusarium spp., may invade the skin of marine mammals which have decreased immunocompetence, having suffered the stress of stranding (sometimes accentuated by transportation and relocation procedures, if still alive) (Frasca et al. 1996). Peeling skin was observed on one female striped dolphin among 17 stranded on a beach in W. Australia but the general pathology encountered (dehydration, muscle trauma) largely reﬂected damage that occurred as a direct consequence of stranding (Gales 1992). Corpses are of localised occurrence but provide a rich protein food source (Mason 1976). That stranded whales used to be important historically as food to coastal human communities in isolated regions is testiﬁed by displays in the Húsavík whaling museum, Iceland (P.G.M., pers. obs.). Few comments on the ecological impact of cetacean carcass decomposition on beaches have been discovered in the literature. However, Chatto & Warneke (2000) reported that substances emanating from the rotting carcass of a 13-m sperm whale (Physeter macrocephalus) stranded in the Northern Territory of Australia killed mangroves over a radius of 100 m. Killer whales (Orcinus orca) are well known as predators of other marine mammals, from sperm and baleen whales to pinnipeds, sea otters and dugongs (Jefferson et al. 1991). The spectacular behaviour shown by killer whales, of intentionally stranding themselves onto beaches, was ﬁrst described by Lopez & Lopez (1985), and subsequently became world-famous (see Attenborough 1990). The intentional stranding of killer whales on the beaches of Possession Island, Crozet Archipelago, both in order to capture elephant seals and during social play, was described by Guinet (1991) and Guinet & Bouvier (1995). 528

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Bottle-nosed dolphins occur in two ecotypes: a nearshore and an offshore form. The name Tursiops truncatus was retained in the IUCN Red Data Book (IUCN 1991) as an inclusive term, covering all nominal species. Data from South African specimens suggest that the T. “truncatus” form feeds further offshore than the T. “aduncus” form. The primary prey of the coastal form are ﬁshes and invertebrates inhabiting the littoral (i.e. during high tide) and sub-littoral zones (IUCN 1991). In shallow water, individuals commonly chase prey independently, sometimes along mud lines created by surf, tides or currents near steep drop-offs. They also hunt co-operatively, juveniles learning from adults (Wells et al. in Reynolds & Rommel 1999), with several dolphins perhaps driving ﬁsh into shallow water (Hoese 1971). T. aduncus was described recently by Connor et al. (2000) foraging among seagrasses in shallow water (<4 m) in Shark Bay, Western Australia. Dolphins foraging by “bottom grubbing” would sometimes chase, surface with, or throw ﬁsh. While foraging over tidal ﬂats (1.5–2.5 m) bottom-grubbing individuals would sometimes perform peculiar ﬂukeslaps that produced a 1–3.5 m high splash of water and an audible “kerplunk” sound in air. These sounds may aid in the location or capture of ﬁsh by eliciting a startle response from hiding ﬁsh and thereby revealing their location to the foraging dolphin. “Fish whacking” behaviour by dolphins has been described by Wells et al. (in Reynolds & Rommel 1999), involving dolphins stunning ﬁsh by striking them with their ﬂukes, often sending them ﬂying into the air in shallow seagrass meadows. Sometimes dolphins feeding in tidal saltmarsh creeks in the southeastern United States chase ﬁsh out of the water onto mudbanks (“strand feeding”), retrieving their catch by jumping out after them (Hoese 1971), a capability exhibited so spectacularly by captive dolphins in oceanaria worldwide. Consistently, in South Carolina, bottlenose dolphins beach on their right side, suggesting they may be right-eyed in the near ﬁeld (Petricig 1995, see Wells et al. and illustrated by them in Reynolds & Rommel 1999). Indo-Paciﬁc hump-backed dolphins (Sousa chinensis) also chase schooling ﬁsh up onto sandbanks, then slide up to grab them without getting stranded (Chatto & Warneke 2000).

Carnivora Tigers The Bengal tiger (Panthera tigris) roams the 4264 km2 of mangal vegetation in the Sunderbans tiger reserve (Ganges delta, India). Owing to a neotectonic movement, the Bengal basin tilted eastwards during the twelfth to sixteenth centuries. This resulted in large-scale reduction of freshwater ﬂow, increase in salinity and accelerated tidal action from seawards. This caused progressive extinction of wild animals like Javan rhinoceros, water buffalo, swamp deer, barking deer, gharial and sweet-water turtles from the Indian Sunderbans. Along with terrestrial prey (wild boar, cheetal, monkey) the mangrove tigers also feed on ﬁshes, crabs and turtles (Sanyal 1992).

Foxes D. Morritt (pers. comm.) reports having encountered nocturnally foraging red foxes (Vulpes vulpes) on salt marshes near Bristol (UK) on several occasions. Radio-collared foxes were found by Meek & Saunders (2000) to have a mean home range of 135 ha and core activity 529

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Table 5 Comparison of the proportions of prey items in the diet of red foxes (Vulpes vulpes) on the Sands of Forvie National Nature Reserve, Aberdeenshire (Scotland) in 1974, 1977–79 and 1988 (Adapted from Wilson, 1990; see therein for original references). Est. wt. = estimated weight (g). NA = Not available. N = Number. Prey

1974 Est. wt

Lagomorphs Small mammals Other mammals/carrion Eiders Other ducks Other birds N

1977–79 % wt

4395 70 84 1 827 13 61 1 449 7 438 7 36 scats + 1 stomach

1988

Est. wt

% wt

Est. wt

% wt

NA NA NA NA NA NA

68 2 <1 13 9 8

1152 40 100 1269 75 12

44 2 3 47 4 <1

60 scats

31 scats

areas of 23 ha in coastal habitats in New South Wales, Australia, with no signiﬁcant differences between dogs and vixens. Arctic foxes (Alopex lagopus), commonly assigned the role of scavenger of marine mammal remains left by polar bears (Smith 1976), have also affected a variety of seabird species on the islands off Alaska, and attempts to remove fox populations have been quite successful (Furness & Monaghan 1987), for instance, allowing restoration of island populations of black oystercatchers (Haematopus bachmani) and pigeon guillemots (Cepphus columba) (Byrd et al. 1997). Tinbergen (1953) regarded red foxes as a major predator of herring gull (Larus argentatus) eggs and chicks. The rabbit used to be the main prey of red foxes in the dunes of the Sands of Forvie National Nature Reserve in Aberdeenshire (Scotland) and, as recounted by Wilson (1990), it had been thought that eider ducks (Somateria mollissima) might have been “relatively unpalatable”. However, in 1988, the proportion of rabbits in fox diets there dropped considerably, as did the proportion of non-duck items. The only species to increase in dietary prominence was the eider (Table 5). It is possible that this change was prompted less by a decline in rabbit availability than by an increase in eider availability. This increase in eider availability was caused by the removal of the protection hitherto afforded to nesting ducks by an electric fence (Wilson 1990). Quinlan & Lehnhausen (1982) similarly reported Arctic foxes (Alopex lagopus) predating on nesting common eiders in Alaska. Oystercatchers (Haematopus ostralegus) may be taken by red foxes too, but this seems to be an unusual event (Goss-Custard et al. 1996). Red foxes were regarded by Owen (1980) as being of only minor signiﬁcance as predators of geese at the population level. He claimed that losses of adults during moult, although they do occur, are of negligible importance. However, Ellwood & Ruxton (1970) stated that red fox predation can be a major problem to geese. Not only do they take the sitting goose and the eggs but, unless the parents can get the goslings to water soon after hatching, they will take the brood and possibly one or both parents, as they attempt to protect their offspring. Once the brood is on the water they are reasonably safe from foxes. Arctic foxes are also substantial predators of many geese species: black brant (or brent) (Branta bernicla nigricans), barnacle (Branta leucopsis), pink-footed (Anser brachyrhynchus), 530

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lesser snow (Chen caerulescens caerulescens), Ross’s (Chen rossii) (Anthony et al. 1991, Madsen et al. 1992, Pielou 1994, Bantle & Alisauskas 1998, Summers et al. 1998). The presence of Arctic foxes led to the failure of the Svalbard brent goose population to nest in summer of 1989 (Madsen et al. 1992), and resulted in dark-bellied brents foregoing breeding in the Pyasina delta in Taimyr (Russia) at the nest initiation stage (Spaans et al. 1998). Summers et al. (1998) found a negative relationship between Arctic fox numbers or occupied dens and the breeding productivity of brent geese in the following year. Birkhead & Nettleship (1995) compared breeding success on islands in Labrador with and without Arctic foxes. On islands with foxes, razorbills (Alca torda), common murres (= guillemots) (Uria aalge) and thick-billed murres (U. lomvia) had ceased breeding. Atlantic pufﬁns (Fratercula arctica) continued to attempt to breed but fox predation on adult birds and eggs was intense and breeding success was low. On islands visited by foxes earlier in the season, the number of breeding razorbills was reduced, and breeding by common murres was delayed by 2–3 wk. On the western coast of Svalbard, the diet of Arctic foxes comprised alcids (mainly little auks, Plautus alle, and Brünnich’s guillemot (= thick-billed murre), U. lomvia), gulls (mainly kittiwakes) and fulmars in summer (Frafjord 1993). In Spitsbergen, the Arctic fox had a close connection with colonies of little auks (Norderhaug 1970). Dens of one or more fox families were normally found close to big colonies of little auks, and in those localities the diet of the foxes consisted mainly of that species during the summer. When the little auks left their colonies in the second half of August, the foxes switched to less attractive species, like Arctic terns (Sterna macrura). The capacity of Arctic foxes to wreak havoc with breeding bird colonies represents a threat to rarities like the ivory gull (Pagophila eburnea) (Pielou 1994). The dense colonies of cliff-nesting seabirds in the west fjords of Iceland attract arctic foxes, which have even been reported capturing birds in ﬂight from the cliff top (Harlow 2000). Arctic foxes in coastal regions of Iceland feed mainly on prey derived from the sea, including seaweeds and seal carcasses in their diet as well as seabirds (Hersteinsson & Macdonald 1996). Angerbjorn et al. (1994) studied dietary variation in Arctic foxes from various inland and coastal situations in Iceland, Greenland and Sweden using stable carbon isotopes (δ13C) extracted from lower jaw bone collagen. Coastal foxes from Greenland had typical marine values of −14.9‰, whereas coastal foxes from Iceland had intermediate values of −17.7‰. Arctic foxes from coastal habitats in Iceland carry higher helminth burdens and more parasitic species per host than foxes from inland sites (Skirnisson et al. 1993). In winter, fulmars, and in one region, seals, were important foods of Arctic foxes in Svalbard (Frafjord 1993). Working on Arctic foxes in the Yukon–Kuskokwim delta in western Alaska, Anthony et al. (2000) found that indices of subcutaneous fat decreased annually in April–May when the occurrence of carrion of marine mammals was highest. Foxes with small mammal remains in their stomachs were captured further from the Bering Sea coast than those without small mammal remains. Conversely (and not surprisingly), foxes consuming remains of marine mammals were closer to the coast. Arctic foxes proved to be signiﬁcant predators of ringed seal pups (Phoca hispida) on western Victoria Island, Canada. They entered the subnivean birth lairs through the snow dome to kill pups. An average pup predation of 26.1% was estimated over a 3-yr of study. Foxes appeared to use the whole carcass, suggesting that they occupied the lair for some time after the kill (Smith 1976). The swimming ability of Arctic foxes was observed by Strub (1992), who described one interrupting a 2.1 km swim by resting in the sun on an ice ﬂoe in Alexandra Fjord, eastern Ellesmere Island in mid-July 1990. The relationships between Arctic foxes, lemmings 531

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(see also Angerbjorn et al. 1999, Elmhagen et al. 2000) and brent geese may be further inﬂuenced by snowy owls, which create fox-exclusion zones around their nests, thus providing safe nesting areas for geese (Summers et al. 1998). Cliff-nesting barnacle geese in Greenland are subject to 25% gosling losses to gulls and foxes as they leave the cliff ledges (Cabot et al. 1984). The Arctic fox, a lemming specialist according to some (Elmhagen et al. 2000), during “low lemming” years, switched predation to king eider duck (Somateria spectabilis) nests as a result of increased search effort for food on Traill Island, Northeast Greenland (Sittler et al. 2000). Samelius & Lee (1998), however, claimed that despite several studies on interactions between Arctic foxes and geese, there was no documented observation known to them of an Arctic fox killing a nesting goose. They suggested that such mortality could be relatively uncommon and that most goose parts in Arctic fox diets were the result of scavenging on goose carcasses, or killing of goslings and moulting adults during the brood-rearing season (see also Pielou 1994). On Banks Island, they observed an Arctic fox taking eggs from lesser snow geese (that had been pushed off nests by charging muskox, Ovibos moschatus), and thought that such opportunism characterised the diet of Arctic foxes. During nesting at a large goose colony, each year foxes took on average 900–1570 lesser snow goose eggs per fox on Banks Island, Canada (Pielou 1994). Elmhagen et al. (2000) styled Arctic foxes as “opportunistic specialists”. The diet of Arctic foxes in Greenland varied signiﬁcantly between areas, and included seaweed, birds and birds’ eggs, ﬁsh, shellﬁsh (inter alia) (Kapel 1999). A similar varied diet (see above) was reported for Arctic foxes in Iceland by Hersteinsson & Macdonald (1996). According to Samelius & Alisauskas (2000), Arctic foxes took mostly eggs when foraging among lesser snow geese, and most of these eggs (97%) were cached for later use (Pielou 1994). Eighty-three per cent of adult geese taken were eaten immediately. In years with high fox abundance, foxes spent considerable effort moving eggs from old caches, either because of – or as a deterrent to – cache pilfering. In contrast to Pielou’s comments above, Frafjord (1993) found that Arctic foxes on the western coast of Svalbard frequently cached food by scatter hoarding, placing only a single item in each cache. More furtive and reclusive than the Arctic fox (Pielou 1994), red foxes raided 89% of nests of loggerhead turtles (Caretta caretta) at Dalyan, Turkey, caching at least some eggs from 88% of them (MacDonald et al. 1994, note also Brown & MacDonald 1995 on Chelonia mydas egg predation). Of the eggs taken by foxes, 48% were scatter-hoarded and the rest were eaten or ignored. Foxes usually carried eggs inland, and cached them on topographically distinct features, such as spurs of pebbles. Foxes retrieved 94% of caches, 80% on the subsequent night. Most caches were made by a single adult, but almost all were recovered by an adult accompanied by young, suggesting that foxes were storing turtle eggs speciﬁcally to feed their offspring. An Arctic fox encountered by Sklepkovych & Montevecchi (1996) on an offshore seabird island in Newfoundland hoarded seabirds in larders. In both red and Arctic foxes, larder storage is associated with a superabundance of prey and appears to represent a ﬂexible response to environmental conditions. The importance to Arctic foxes in spring (i.e. before geese arrive in the Canadian Arctic), of having geese and eggs cached from the previous summer was stressed by Bantle & Alisauskas (1998). The large increases in lesser snow geese and Ross’s geese in recent years may have had a beneﬁcial effect for Arctic fox populations over a large area (Bantle & Alisauskas 1998).

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Hyenas There are a few places where shores provide much of the food for terrestrial mammals, for example, the Skeleton Coast (Namibia) for brown hyenas (Hyaena brunnea) in particular (pers. comm. from P. Bright to D. Morritt). Two clans of brown hyenas were studied by Skinner et al. (1995) on the arid chilly Namib desert coast where large seal colonies exist. Carcasses of seals (Cape, or South African, fur seal; Arctocephalus pusillus) were abundant during summer and autumn following the seal pupping season, and scarce in winter and spring. Hyenas fed predominantly on carcasses; mostly scavenged but occasionally killed by them. Time spent foraging was not affected by carcass availability but the dispersal of food had repercussions for territory and group sizes. Overabundance of food resulted in the maintenance of territories extending beyond the distribution of food. The shape and area of these territories may result from cultural inheritance of land.

Jackals Small scattered, but resident, populations of golden jackal (Canis aureus) occur along the coasts of the Balkan peninsula (Krystufek et al. 1997). Brown & MacDonald (1995) studied the factors affecting the nesting success of green turtles (Chelonia mydas) in Turkey. Over 75% of nests were preyed upon either by red foxes (Vulpes vulpes) or golden jackals. The siting of turtle nests, but not their susceptibility to predation, was affected by the location of the vegetation line, the slope of the beach and the proximity of a tourist development. The black-backed jackal (Canis mesomelas) relies on marine resources. On the coast it takes virtually no terrestrial prey (Avery et al. 1987). These authors investigated 13 jackal middens on small sand hummocks within 25 m of the beach along the Skeleton Coast, Namibia. Bird remains were the most common items (67.6%) recovered, followed by mammals (17.0%) and ﬁsh (15.1%). Canis mesomelas is an unselective opportunist, scavenging whatever it encounters along the strandline. In a later study, Nel et al. (1997) studied prey use by black-backed jackals along this coast. Bird remains again showed the highest frequency of occurrence, and highest relative frequency, in jackal faeces in all samples from Namibia except for the Skeleton Coast summer sample, wherein seal remains were the most common (Fig. 7). Most prey items, and in every sample the dominant prey category, were of marine origin. Black-backed jackals are largely dependent on seals (Cape fur seal; Arctocephalus pusillus) at Van Reenen Bay in Namibia (Oosthuizen et al. 1997). During the pupping season, seal placentas and dead pups were abundant. An estimated 1770 pups were killed or scavenged annually by jackals there, and their diurnal activity cycle varied according to the availability of such food items. All jackal kills were of seal pups. These authors suggested that jackal predation may be a factor in preventing mainland non-breeding seal colonies from developing into breeding colonies.

Mongooses Predation on sea turtle eggs and nests by the small Indian mongoose (Herpestes auropunctatus) has been described by Nellis & Small (1983). This species was widely introduced onto islands of the Caribbean and Paciﬁc in the late nineteenth century to control rats on sugarcane plantations. Mongoose proved to be major and persistent predators of hawksbill turtle (Eretmochelys imbricata) nests on all the US Virgin Islands inhabited by mongoose, targetting

533

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Figure 7 Black-backed jackal (Canis mesomelas): top) among Cape fur seals (Arctocephalus pusillus) (Photo: Jan Nel) and; bottom) eating a seal carcass in the surf zone, Cape Cross, Skeleton Coast, Namibia (Photo: Rod Braby).

either freshly deposited eggs or nests just prior to hatchling emergence (note also Seaman & Randall (1962) on mongoose predation on a green turtle (Chelonia mydas) nest there). Though typically a solitary feeder, group feeding by Herpestes auropunctatus facilitated the spread of the habit of feeding on sea turtle eggs (Nellis & Small 1983). The water mongoose (Atilax paludinosus) is common on saltmarsh habitats in South Africa where they feed mostly on shore crabs (Cyclograpsus) (C. L. Grifﬁths, pers. comm.). The diets of water mongoose living along the coast and inland, as derived from faecal 534

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analysis, were compared by Louw & Nel (1986). The most important prey items of coastal water mongoose were amphipods and the common shore crab, C. punctatus. Similarly, the diets of the yellow mongoose (Cynictis penicillata) occurring in Karoo and West Coast Strandveld ecosystems in South Africa were compared by Avenant & Nel (1992). Mist, rain and cloudiness were factors affecting the activity pattern of yellow mongoose in a coastal region of Cape Province, South Africa (Cavallini 1993). Rodents and reptiles were important as food on the coast, and only at the coastal site did dietary patterns reﬂect ﬂuctuations in prey availability. Cavallini & Nel (1990) analysed 234 scats from the Cape grey mongoose (Galerella pulverulenta) from an area of the South African West coast. There, small mammals made up the bulk of the diet, with insects constituting a secondary food source. Hockey (1996) relates how the oystercatcher (Haematopus moquini) will drive the small grey mongoose away from its nest, rather than resort to distraction displays.

Raccoons Ricketts & Calvin (1968) noted that raccoons (Procyon lotor) visit the shore at low tide from Puget Sound to Baja California to feed on crabs. This species was implicated by Erwin et al. (2001) as feeding on ground-nesting waterbirds in the Virginia barrier islands, USA. P. Bright (pers. comm. to D. Morritt) observed raccoons emerging onto the sand of a beach in Costa Rica from adjacent fringing forest to feed, but only at weekends when there were people and food litter about. Raccoons were regarded by Anonymous (1997) as a major source of turtle nest destruction and egg predation in the USA, particularly on Key Island, Florida. Sea turtles are federally protected in the USA and raccoon predation on eggs and hatchlings can signiﬁcantly decrease hatchling productivity on barrier beaches in the southeastern States. Managers of many sea turtle nesting beaches reduce raccoon populations to minimise nest predation. However, it is unknown whether removal of raccoons may have additional ecological consequences because they play other ecological roles, for example, in seed dispersal, predation on other vertebrates and invertebrates (Ratnaswamy & Warren 1998). In Georgia, USA, raccoons can remain in the Spartina zone of salt marshes at high tide by constructing a ramp. They bend over the tops of Spartina plants to form a platform on which they can lie during the high tide (Green 1968). The crab-eating raccoon, or agouara (Procyon cancrivorus) is a skilful predator of ﬁddler crabs (Uca sp.) in mangrove forests in northern Brazil (Fernandes 1997).

Coyotes Ricketts & Calvin (1968) ﬁrst noted that in Baja California, where the barren back country offers little nourishment, the coyote (Canis latrans) is a consistent intertidal feeder. How the distribution and abundance of coyotes was inﬂuenced by the input of food from the sea in the arid deserts of Baja California has been analysed by Rose & Polis (1998). They found that coyote abundance was 2.4–13.7 times that in adjacent mainland areas. These high population densities were possible because coastal coyotes were subsidised by the ﬂow of abundant and diverse resources coming directly and indirectly from the ocean. Analysis of scats revealed that the dietary breadth of coastal coyotes was expanded and that much more food was consumed by individuals and populations of coyotes in coastal regions. Typically, scat mass was more than double that at inland sites. An average of 47.8% of all items found in coastal scats emanated from the sea. 535

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Feral cats Cats (Felis catus) and rats (Rattus spp., see p. 522) are generally considered to be the most serious alien predators of seabirds, and are widespread (Moors & Atkinson 1984, Furness & Monaghan 1987). Burrowing petrels are the main prey of feral cats on Marion Island, Indian Ocean (Williams 1978, Van Rensburg 1985) and a cat eradication programme has been in progress there. Stomach contents of killed cats showed a signiﬁcant decrease in the frequency with which burrowing petrels were consumed from 1975 to 1989. Birds were still the preferred prey but, because of the reduction in the number of birds as a result of cat predation, mice (Mus musculus) became an increasingly important component of the diet of the remaining cats (Bloomer & Bester 1990). Young pufﬁns were preyed upon by feral cats and rats on the Shiant Isles, West Scotland (Atkinson 1949). He noted that summers must have been “softliving” times for the cats but that the winters must have been lean. Their subsistence then must have been based on dead ﬁsh, oiled seafowl, braxy mutton, shellﬁsh and possibly even rats. Feral cats have affected burrowing petrel populations on the French sub-Antarctic islands (Chapuis et al. 1994). Cats are known to have eliminated, or severely reduced, colonies of black-vented shearwaters (Pufﬁnus opisthomelas), Cassin’s auklets (Ptychoramphus aleuticus) and Xantus’ murrelets (Synthliboramphus hypoleucus), and to have caused the extinction of the endemic Guadalupe storm petrel (Oceanodroma macrodactyla) (McChesney & Tershy 1998). Feral cat predation of a sooty shearwater (Pufﬁnus griseus) chick was reported from New Zealand by Lyver (2000). Although seabirds proved to be only a minor item in the diet of feral cats on Raoul Island (Kermadecs), this was probably because of past depredations spanning a century or more (Fitzgerald et al. 1991).

Domestic and feral dogs Keller (1991) noted the frequent disturbance to common eider ducklings (Somateria mollissima) on the Ythan estuary, Aberdeenshire (Scotland) caused by recreational activities of people, including walking along shores exercising dogs (Canis lupus familiaris). Disturbance of duckling creches could last for 35 min, and could lead to an increase in predator encounters during the 5 min following the disturbance. The observation made by McAlpine (1996) of a female common eider hatching a clutch of gadwall (Anas strepera) eggs on an island in the Bay of Fundy is of interest in this respect. He attributed this aberrant behaviour to high intensities of eider egg predation by gulls brought about by increased human activity on the island disturbing nesting eiders. Egg predation by dogs has been linked to the local ﬁshing industry in India. Piles of unwanted ﬁsh left behind on creek banks along the coast of Kodikarai (South-east India) attracted large numbers of feral dogs into three little tern (Sterna albifrons) colonies and these suffered a much higher rate of egg predation than two others not so affected by ﬁshers (Holloway 1993). The native St Kildans trained mongrel dogs to drive pufﬁns from their burrows (Steel 1975). Marine iguana (Amblyrhynchus cristatus) remains were found in 58% of feral dog faeces examined on Santa Cruz and Isabela Islands (Galápagos Islands) by Kruuk & Snell (1981). In 51% they were the sole or most important prey item. On Isabela Island the risk of nocturnal predation was signiﬁcantly greater for larger iguanas. Variation in predation risk was related to differential ﬂeeing distances and to greater exposure of territorial male iguanas, which did not seek shelter at night. These authors speculated that predation by dogs was probably greater than that which could be sustained by the population and recommended 536

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eradication of the dogs. Marine iguanas have not been the only organisms to suffer on the Galápagos Islands because of feral dog predation; giant tortoises (Geochelone elephantopus) and colonially nesting birds, for example, ﬂightless cormorants (Nannopterum harrisi) and blue-footed boobies (Sula nebouxi), have also been affected (Barnett & Rudd 1983). The predominance of small prey that they found in the dogs’ diet (cf. Kruuk & Snell 1981) dispensed with the need for dogs to hunt in large packs; groups of three individuals were usual. A co-ordinated eradication programme has been underway on Isla Isabela since 1979 (with marked success). Control was based on poisoned baits (compound 1080; sodium monoﬂuoroacetate) carefully placed (Barnett & Rudd 1983). Village dogs on the islands were recorded scavenging along the seafront, taking the occasional lava lizard (Tropidurus albemarlensis), rat or Sally Lightfoot crab (Grapsus grapsus) to supplement their diet (Barnett & Rudd 1983). Dingoes in Australia (Canis lupus dingo) interbreed with feral dogs but some populations are more or less pure dingo (e.g. central Queensland and Fraser Island where only 5% and 17% hybrids, respectively were reported: Woodall et al. 1996). Fraser Island dingoes forage intertidally. Their interaction with tourists has recently (April 2001) resulted in the tragic death of a child, prompting demands for a dingo cull. The most visible marine environmental impact of domestic dogs to the general public relates to fouling of beaches with dog faeces. Dog faeces may contain canine parvovirus (CPV) (Esfandiari & Klingeborn 2000) and Toxocara canis (especially prevalent in strays; see O’Sullivan 1995). In 1996/7, 34% of people in England and Wales were “very worried” about fouling by dogs, exceeding the proportions of those people who were “very worried” about acid rain (31%) or overﬁshing of the seas (30%) (Salter & Ford 2001, table 3). Clearly, in the UK, most people’s environmental concerns still relate primarily to the health and welfare of the individual. A wider scatological issue relates to the known repellant effect that the odour of dog faeces has on consumption of contaminated food by grazing ungulates (Arnould et al. 1993, Terlouw et al. 1998). This repulsion may last for several days (Arnould et al. 1993). The active chemicals in dog and wolf faeces that suppress feeding in sheep seem to be fatty acids mixed with neutral compounds acting synergistically (Arnould et al. 1998). The increased snifﬁng that, in cattle, accompanies the presence of such an odour source and results also in post-feeding “stretched” locomotion (Terlouw et al. 1998) indicates awareness of potential dangers posed by carnivore presence (note also Ward et al. 1997).

Badgers In 1994, a badger (Meles meles) entered an important colony of Audouin’s gull (Larus audouinii) in the Ebro Delta (northwest Mediterranean), disturbing the gulls and predating on nests (taking both eggs and chicks). This colony had grown to represent the majority of the world population of Audouin’s gull (65% in 1997). Gull recruitment post-1994, however, was not affected by high nest predation by the badger, although after the event, the proportion of Ebro Delta birds nesting on the nearby Columbretes Islands tripled (Oro & Pradel 2000). The northern European distribution of the badger now extends along the Baltic coast to the inner part of the Gulf of Bothnia in Sweden, and through coastal and inland areas, up to 65°N in Norway, that is, beyond the Arctic Circle (Bevanger & Lindstrom 1995). On the Tyrrhenian coast of Italy, the use of more latrines by badgers (Meles meles) at times of food shortage might be the best way of marking the territory to inform intruders of 537

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the identity of the territory owner, thus allowing defence of valuable food resources (Pigozzi 1990).

Otters The Eurasian otter (Lutra lutra) feeds in the intertidal zone of rocky shores especially along the western seaboard of Scotland, pairing up (Thompson 1931) and making their homes (holts) on the shore (Kruuk & Hewson 1978), maybe in cliffs or under rock piles (Lawrence & Brown 1967), during the winter months (Yonge 1966). In their otter survey of Scotland (1991–94), Green & Green (1997) reported that coastal sites were over-represented in the negative group (otters absent) and under-represented in the newly positive group (sites where earlier surveys had recorded absences). Splitting the coastal sites geographically also revealed the poor performance of the East coast compared with the West coast and islands (Table 6). All negative coastal sites in the Highlands area were on the East coast. The otter is a powerful swimmer and is the only non-introduced mammalian carnivore in the Outer Hebrides (Murray 1973). Along the mainland west coast of Scotland otters are predominantly nocturnal and piscivorous (Thompson 1931, Kruuk 1995) taking relatively small prey: lumpsucker, wrasse, pollack, butterﬁsh, eels and scorpion ﬁsh (Mason & MacDonald 1986, Boyd & Boyd 1996, Kruuk & Carss 1996), as they do in the Shetland Isles (Herfst 1984, Kruuk et al. 1987, Kruuk & Moorhouse 1990, Nolet et al. 1993). Otters are omnivorous and will take carrion (Mallinson 1978) but primarily they eat ﬁsh (Harris 1968, Macdonald 1990, Kruuk 1995). Elmhirst (1938) recorded otters taking crabs (Carcinus maenas, Cancer pagurus and Necora puber) in that order of signiﬁcance, as well as isopods and amphipods, on the Isle of Cumbrae in the Firth of Clyde. One was witnessed many years ago to take limpets (Patella sp.) from the shore on Foula, the remotest of the Shetland Isles (P. G. M., pers. obs.). Harris (1968) reported otters “catching and peeling” lobsters. From a January collection of 50 spraints taken from the shores of Loch Broom, a sheltered sea loch in Scotland, Mason & MacDonald (1986) found 130 items of which the most important were crustaceans, mainly shore crabs Carcinus maenas. No other invertebrates were found. Bird remains were found in three spraints. All the remaining items were of ﬁsh (14 species), especially butterﬁsh (Pholis gunnellus) and Yarrell’s blenny (Chirolophis ascanii). The caloriﬁc return of different prey species, however, varies considerably: thus an

Table 6 The distribution of coastal sites unoccupied and newly occupied by otters (Lutra lutra) in Scotland from 1991–1994. Newly occupied sites are deﬁned as those occupied for the ﬁrst time on third survey. Coastal = coastal sites at elevations between 0–5 m without burns; Coastal burns = sites where small streams ﬂowed directly into the sea (modiﬁed from Green & Green 1997). Habitat

No. of sites

West coastal and islands East coastal Total coastal Coastal burns

294 80 374 378

Total, all sites

752

No. unoccupied

No. newly occupied

% unoccupied

% newly occupied

25 37 62 49

29 12 42 29

8.5 46.3 16.6 13.0

9.9 15.0 11.2 7.7

111

71

14.8

9.4

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eel (Anguilla) or a three-bearded rockling (Gaidropsarus vulgaris) contains more energy than a sea scorpion (Taurulus bubalis) and crabs, respectively (Kruuk 1995). Crabs formed a relatively small proportion of the diet of otters in Shetland (8%) (Watson 1978). There was a marked difference between the foraging of a cub v. its dam, with 13 out of 61 items caught by the cub being crabs, whereas the mother caught none (Watson 1978, Watt 1993). This suggests that crabs are easier to catch, but that the low cost of foraging is matched by low return of energy. Watt (1993) found that cubs on the island of Mull (Scotland) began to capture a small proportion of their own food by 5 months of age and that this proportion increased with age. The low foraging efﬁciency of juvenile otters was suggested by Watt (1993) to be responsible for the extended period of parental care in Lutra lutra. Spring was a stressful feeding time in Shetland when otters ate “low quality” food, like crabs, sticklebacks and other light-weight prey (Kruuk & Moorhouse 1990). Fish must be landed before being eaten, and otters may be wasteful feeders when food is plentiful. Thompson (1931) recounts historical observations made by Charles St John of poorer people in Scotland visiting the regular landing places of otters in the early morning for the purpose of obtaining a goodly salmon or trout, which might be little the worse for the loss of the portion taken by the otter (see Kruuk 1995 ﬁg. 4.22). The characteristics of such traditional landing places would seem to be seclusion and ease of exit from the water at all states of the tide (Thompson 1931). Food caching in captive otters has been reported by Harper & Jenkins (1982), but this seems to be unusual behaviour (cf. other surplus killers like mink and fox). Population densities of otters in Shetland were some 0.8 otters km−1 of coast (Kruuk & Carss 1996). Temporal and spatial patterns of rest site use by four female otters along the southwest coast of Portugal were studied by Beja (1996). Rest sites were scarce for marinefeeding otters there because they have an obligatory association with freshwater sources and these are infrequent and scattered along the coast. Most rest sites were located within dense bramble (Rubus) thickets along coastal and estuarine streams. Bowyer et al. (1995) studied habitat selection and home ranges of river otters (Lutra canadensis) living along the coastlines of Prince William Sound, Alaska >1 yr after the Exxon Valdez oil spill. Home ranges of otters were about twice as large on the oiled area as on the non-oiled area, suggesting that habitat quality for otters was reduced as a result of the oil spill. Deposition of faeces by otters at latrine sites was signiﬁcantly less at those sites that were heavily oiled compared with non-oiled sites. Otters selected steeper tidal slopes and sites with larger rocks on oiled sites than on non-oiled sites. They avoided shallower slopes and protected areas with smaller rocks and gravel where the oil persisted the longest. River otters abandoned latrine sites (an index of their abundance) over three times more often in oiled sites (Duffy et al. 1994). Coastal oil spills can thus have chronic impacts, physiological as well as ecological (see Duffy et al. (1994), on mammals as well as on birds and invertebrates). Signiﬁcant effects on river otter blood and enzyme chemistry using non-lethal sampling were recorded by Duffy et al. (1994) 2 yr after the spill and following the major clean-up procedure. These effects included elevated concentrations of blood haptoglobins, and interleukin-6ir, as well as elevated activities of aspartate aminotransferase, alanine aminotransferase and creatine kinase. Eurasian otters in Shetland took mainly long-bodied ﬁshes (eels, butterﬁsh, blennies) followed by demersal species (Cottus spp., lumpsuckers), with ﬂatﬁshes being taken least frequently and open-water species hardly at all (Kruuk 1995). Whether this should be interpreted as prey preference or is simply a reﬂection of availability is uncertain. Littoral ﬁshes on rocky shores tend to be elongate species (Gibson 1969, 1982). Coastal otters will even 539

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tackle conger eels (Harris 1968). Kruuk (1995) regarded this ability to cope with unusually large prey as an example of otter family life. When a female is providing for her cubs she eats small prey herself, and takes larger prey to her offspring. This behaviour makes energetic sense; the food for the cubs has to be ferried over much longer distances than her own food and she can only take one item at a time to the holt. Otters prefer to dive in shallow (0–3 m depth) water (Nolet et al. 1993). In their view, the fact that such foraging results in the capture of demersal species (eelpouts Zoarces viviparus, rocklings Ciliata spp., Gaidropsarus spp., sea scorpions Tauralus bubalis and butterﬁsh Pholis gunnellus), was evidence that they dive to the bottom to feed. By concentrating their foraging in shallower depths, otters incur lower thermoregulatory costs and beneﬁt from shorter travel times (Nolet et al. 1993). Energy expenditure during foraging is high due to heat loss, determined largely by water temperature. Water is a better conductor of heat than air and otters lose heat rapidly. Consequently they have well greased fur that is thick and close to trap air for insulation purposes (Thompson 1931). An unfortunate consequence of their coastal diving habit is that they are often drowned in eel fyke nets and lobster creels (Harris 1968, Twelves 1983). Some otter foraging, however, must occur in the water column if pollack is to be captured, for it is a pelagic species, but one associated with weed (R. Gibson, pers. comm.). Prey selection in coastal Eurasian otters has been studied by Watt (1991) and Heggberget & Moseid (1994). Otters are not exclusively piscivorous, as predation on rabbits and poultry (Thompson 1931, Kruuk 1995), on duck, cormorant, gannet (Harris 1968) and other seabirds has been recorded; for example, on black guillemots (Ewins 1985) and storm petrels (Quinlan 1983). Nevertheless, at a critical level of prey availability, a relatively small reduction in net energy intake would make hunting unproﬁtable in a given area. This dilemma is particularly obvious in cases where hunting is relatively energy expensive (as when foraging in cold water) (Kruuk & Carss 1996, McCluskie 1998). Like most carnivores, otters need long resting periods each day (Nolet & Kruuk 1989, Kruuk & Carss 1996). An otter’s range is distinctive in being linear; a narrow strip on either side of an interface between water and land, where food is acquired in the cold, watery inhospitality of one side and recovery from this exposure takes place on the other. Otters are relatively clumsy on land (compared say with a fox). In water an otter can move fast, but it cools rapidly, probably because a really efﬁcient thermo-insulation would interfere with locomotion on land (as in seals) (Kruuk 1995). The sea otter (Enhydra lutra) originally ranged around the Paciﬁc rim from Hokkaido (Japan) to Baja California (National Research Council 1999). Sea otters became increasingly scarce by the early 1900s due to overharvesting for their pelts, and in 1911 hunting was banned. The diet of E. lutra varies across its range according to prey availability: bivalve molluscs dominated in Prince William Sound, sea urchins in the Aleutians and sea urchins and crabs in California (Estes et al. 1981). This otter is very much an invertebrate specialist; it takes few ﬁshes (Estes et al. 1981). In California it feeds on red abalone (Haliotis rufescens), sea urchins (Strongylocentrotus franciscanus), purple-hinged scallops (Hinnites gigantea), Californian mussels (Mytilus californianus) and various snails, together possibly with giant chitons (Cryptochiton stelleri) (Limbaugh 1961). On coasts depleted of sea otters by hunters, a number of shellﬁsh ﬁsheries developed, including abalone (Haliotis spp.), whose abundance consistently declined when sea otters reappeared in a given locality. However, with hindsight, it was probably the hunting of otters that reduced shellﬁsh predation and allowed the ﬁsheries to develop in the ﬁrst place (Estes & Van Blaricom 1985). 540

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Sublittorally, otter predation on abalones and sea urchins (Strongylocentrotus spp.), results in their prey being restricted to deep crevices where they are relatively inaccessible (Lowry & Pearse 1973). The classic study of this species is that by Estes & Palmisano (1974), which established sea otters as a keystone species controlling the biomass and abundance of sea urchins, which in their turn regulate benthic algal biomass and productivity. These observations have been extended by Duggins (1980) and Duggins et al. (1989). Otters are capable of great reduction of local sea urchin populations, although in the process they indirectly facilitate kelp growth. Kelp beds provide nursery grounds for inshore ﬁshes and kelp detrital production may enhance the growth of abalone and other benthic invertebrates. Otters consume sea urchins (Johnson 1982) preferentially (Watt et al. 2000) and clearly play a critical role in the structuring and organization of nearshore marine ecosystems (National Research Council 1999 but note Estes et al. 1998). They also take ﬁshes, notably spawning Paciﬁc smooth lumpsuckers, Aptocyclus ventricosus, episodically depending on availability in particular years (Watt et al. 2000). Sea otter predation on a variety of seabirds (caught on the water) has been described by VanWagenen et al. (1981) in the vicinity of Monterey, California: viz. western grebe (Aechmophorus occidentalis), cormorant (Phalacrocorax spp.), surf scoter (Mellanita perspicillata), common loon (Gavia imer) and gulls (Larus spp.). Preferred prey may have been depleted, causing the otters to turn their attention to birds. Interestingly, the diet of the sea otter in the longer established populations differed from that in the areas which had been more recently colonised (Estes et al. 1981). In each case investigated, the type of prey eaten most frequently in the “recent” areas was taken less often by the corresponding long-term colony. In California, sea urchins were replaced in the diet by crabs at Point Buchon and turban snails at Piedras Blancas. At Prince William Sound mussels replaced clams as the most frequent items in the denser population. In the Aleutian Islands the difference was less marked than elsewhere but there was signiﬁcantly lower predation on sea urchins and a corresponding higher frequency of predation on ﬁsh in the dense population. The suggestion is that as sea otter populations increase, their selective predation on preferred species tends to reduce the numbers of that prey type and they have to turn to alternative food (Chanin 1985). Sea otters in Prince William Sound were spatially segregated into predominantly (97%) male areas at the front of the expanding population, and breeding areas with fewer (<33%) males (Garshelis et al. 1984). Besides the sea otter, there is another marine otter species in the Paciﬁc; the sea cat (Lutra felina). This is a very rare species about which little is known and which is conﬁned to western South America, mainly Peru and Chile. There are also coastal populations of smooth-coated otters (L. perspicillata) in Malaysia and clawless otters (Aonyx capensis) in Africa (Chanin 1985, Beuchat 1999). Little is known of the diet of the sea cat, other than that it eats ﬁsh, Crustacea and molluscs (Brownell 1978, Cabello 1978), and nothing is known of the diet of coastal smooth-coated otters (Chanin 1985). There have, however, been detailed studies of the diets of African (or Cape) clawless otters. Van der Zee (1981) collected 1129 scats from the Tsitsikama Coastal National Park on the east coast of South Africa and found over 30 species contributing to the diet. Crabs (Plagusia chabrus, Cyclograpsus punctatus), sucker ﬁsh (Chorisochismus dentex) and octopus (Octopus granularis) between them made up 61% of items identiﬁed and accounted for 86% of the weight of the prey consumed. Plagusia chabrus was the most frequently captured species; it was taken twice as frequently as the other crab species Cyclograpsus punctatus. The greater size of Plagusia chabrus, however, meant that they contributed more than six times the ﬂesh value of Cyclograpsus punctatus consumed. Of the four major items above, the octopus was 541

P. G. M OOR E

taken least often, but again its bulk meant that it still represented an important source of energy. Indeed, excluding the weight of the indigestible skeletal remains from calculations reversed the order of prey signiﬁcance and octopus became the most important despite making up less than one in twenty of the items caught. From estimations of the availability of different ﬁsh species in rock pools, van der Zee (1981) showed that otters preferred to prey on suckerﬁsh. These were easier to catch than sparids, because clawless otters feel for their prey with their sensitive forepaws (hence their facility at extracting crabs and octopuses from crevices). Most food items were small in size (<20 g) and although the otters tended to select the bigger individuals of most species, they seemed to select medium-sized individuals of Plagusia chabrus, perhaps to avoid being caught by the chelae of larger crabs. When foraging, a solitary adult Aonyx capensis in a marine habitat in False Bay, South Africa, preferred hunting in depths of 0.5–1.5 m, despite having a higher hunting success, catching larger more energy-rich prey (ﬁsh) and experiencing the shortest time foraging per catch, at depths of 1.5–2.5 m (Somers 2000). Some of Somers’ data supported the optimal breathing hypothesis, viz. surface and dive times increased for dives to greater depths, although other indications were contrary to this hypothesis (neither diving efﬁciency nor percentage time at the surface decreased with increasing depth). Local clawless otters may be avoiding inshore predators (like great white sharks, Carcharodon carcharias) by conﬁning foraging to shallow waters. They took mostly ﬁsh (50%), followed by crabs (? Cyclograpsus punctatus; 27.8%), rock lobsters (Jasus lalandii; 11.1%) and abalone (Haliotis midae; 5.6%) (Somers 2000). The diet of the clawless otter is thus the mirror image of the Eurasian otter, the former consuming mainly crabs, the latter consuming mainly ﬁshes, although whether this is a reﬂection of preference or prey availability remains to be established (Chanin 1985). In the upper Kairezi River, Zimbabwe, Cape clawless otters competed with eels and trout for crab resources. Evidence collected by Butler & Marshall (1996) suggested that trout selected small crabs, while otters and larger eels ate larger, more abundant crab sizes because they partitioned resources spatially by feeding in different microhabitats. Intraguild predation by otters on eels may beneﬁt trout by reducing competition for aquatic insects. The small-clawed oriental otter (Amblonyx cinereus) is a crab eater, and the smooth otter (Lutrogale perspicillata) a ﬁsh eater in Singaporean mangrove ecosystems (Ng & Sivasothi 2001). Hairy-nosed otters (Lutra sumatrana) live both upriver and near the coast in Sarawak. Small groups of up to six may sometimes be seen hunting for crabs on mudﬂats and for ﬁshes in water channels (Hazebroek & Abang Kashim bin Abang Morshidi 2001). The food habits of the river otter (Lutra canadensis) in the marine environment of British Columbia have been described by Stenson et al. (1984). Fish remains occurred in 99.4% of scats, while crustacean and bird remains were encountered in 7.2% and 4.2%, respectively. The majority of ﬁsh belonged to Embiotocidae, Cottidae, Pleuronectiformes, Blennoidea, Scorpaenidae and Hexagrammidae. Similar ﬁsh dietary composition was revealed from stomach samples in animals collected by trappers. There appeared to be no seasonal variation in diet, except possibly for bird remains. River otters fed opportunistically in the nearshore marine environment concentrating on slower moving ﬁshes as prey.

Mink On British coasts, feral American (or common) mink (Mustela vison) seem to prefer undisturbed rocky shores with a wide littoral zone, which may explain their greater density in 542

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Scotland. Home range size is inversely related to density (Dunstone & Birks 1983). Favoured marine habitat consists of shallow, boulder-strewn beaches or rocky shores with rock pools, backed by pasture, rough grazing or woodland with rabbits. Studying coastal areas in Scotland, Bonesi et al. (2000) reported that mink avoided sites with freshwater streams, preferring to forage in the mid-tide zone. Only when foraging at low-tide or mid-tide did mink forage selectively. They showed no preference for areas rich in prey (their four predictors being position within the tidal zone, abundance and size of rockpools, the nature of the substratum and the presence of freshwater streams) when foraging at high tide. Marine prey of mink is predominantly inshore ﬁsh species (blennies, sea scorpions, rockling, etc), seabirds and crustaceans. Large numbers of sea slaters (Isopoda) may also be consumed (Solway Firth Review 1996). D. Morritt (pers. comm.) reports having observed mink feeding on shore crabs (Carcinus maenas) in Lough Hyne, Ireland on a number of occasions. There are authentic records of mink killing almost every species of (British) mammal, with the exception of other Mustelidae, up to the size of and including rabbits, and including domestic poultry, ornamental and other wildfowl and ﬁsh up to 4 lb (1.8 kg) in weight (Swan 1970). Mink are not primarily scavengers, preferring their prey live. They will kill in excess of immediate needs given the opportunity and cache surplus food (Breault & Cheng 1988, Solway Firth Review 1996), with a propensity greater than an otter’s (Solway Firth Review 1996). Birks & Dunstone (1984) recovered remains of ﬁve species of mammals, together with at least 13 bird species from feral mink dens on the Galloway coast, southwestern Scotland over a 12-month study period. They noted, however, that a number of items were undoubtedly taken as carrion, scavenged from the shore or elsewhere. Rabbits accounted for more than half the numbers in their localities (Table 7). Avian prey items, however, form the focus of mink predation (Bignall 1978). Although their impact on water birds in fresh water is well documented, for example, in Loch Lomond (Bignall 1978) or the upper reaches of the Thames (Ferreras & MacDonald 1999), fewer references exist to their equally devastating impact in coastal situations. Aquatic foraging was particularly important for mink in a coastal situation, with rockpool-inhabiting ﬁshes being especially targetted (29.1% occurrence). Fish predation was more pronounced during the winter months when lagomorph prey was less available. Crustacean prey, particularly the shore crab (C. maenas), occurred frequently in the diet (20.3% of scats) and 2.9% of scats contained isopod remains (Ligia oceanica) (Dunstone & Birks 1987). Crustaceans formed the most important prey of mink at marine sites in the Outer Hebrides. The autumn peak of crab predation corresponded with their inshore breeding migration (Macdonald 1990). Dunstone & Birks (1985) noted that population densities of mink were greatest on rocky coasts (cf. riverine or lacustrine habitats). Breeding success of mink is also greater in coastal habitats, compared with rivers and lochans (Hudson & Cox 1988). Mink are excellent swimmers. Craik (1995) reported that they regularly reach Scottish islands more than 1 km from the mainland. They may even stowaway on yachts and ﬁshing boats (Angus 1993b). About half of a sample of 222 dead adult common gulls (Larus canus) from the west coast of Scotland, studied by Craik (1997a), had been killed by mink. Craik (1997b) described how, over the period 1989–95, feral mink caused widespread whole-colony breeding failures of black-headed gulls (L. ridibundus), common gulls and common terns (Sterna hirundo) at colonies on small islands off the west coast of Scotland, by predating eggs and chicks (see also Angus 1993b). After one or more years of such failure, most of the affected breeding sites held no birds or greatly reduced numbers. Birds ﬁnding mink-free islands 543

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Table 7 The occurrence of vertebrate prey remains recovered from mink dens on the Galloway coast (Scotland) (Modiﬁed after Birks & Dunstone 1984). Species

Frequency

Percentage (%)

Mammals Rabbit (Oryctolagus cuniculus) Brown hare (Lepus capensis) Brown rat (Rattus norvegicus) Hedgehog (Erinaceus europaeus) Lamb’s tail (Ovis aries) Sub-total

50 6 2 1 1 60

52.1 6.3 2.1 1.0 1.0 62.5

Birds Herring gull (ad.) (Larus argentatus) Herring gull (chick) Black-headed gull (Larus ridibundus) Common/Arctic tern (Sterna spp.) Guillemot (Uria aalgae) Razorbill (Alca torda) Woodcock (Scolopax rusticola) Lapwing (Vanellus vanellus) Oystercatcher (Haematopus ostralegus) Pheasant (Phasianus colchicus) Grey partridge (Perdix perdix) Others Sub-total

6 9 2 1 2 1 1 2 1 1 1 9 36

6.3 9.4 2.1 1.0 2.1 1.0 1.0 2.1 1.0 1.0 1.0 9.4 37.4

Total

96

99.9

bred successfully. The consequences of impacts by mammalian predators on breeding seabird colonies on small islands may appear unimportant in isolation, but the effect is additive. The colonies of gulls (Larus spp.) and terns (Sterna spp.) studied by Craik (1998, 1999, 2000, Craik & Campbell 2000) were small by seabird standards: almost always less that 1000 pairs, and usually less that 200 pairs. The primary effect of mink on such colonies was to cause “whole-colony” breeding failure; comparatively few adults were killed. Many such colonies were affected, however, since mink are now widespread. This impact was often repeated annually and, after several disturbed breeding seasons, affected colonies were deserted by breeding birds. In the West of Scotland, this has had two longer-term effects. First, discrete areas (entire archipelagoes, ﬁrths, sea lochs and sounds) have lost most, or all, of their breeding seabirds. Second, regional populations of gulls, terns and black guillemots (Cepphus grylle) have decreased (e.g. by 30–50% during 1987–98). This decrease has happened because successful breeding has been inadequate to replace natural adult mortality from other causes. In contrast, tern and gull colonies (with many equally affected minor species such as eider (Somateria mollissima), merganser (Mergus serrator), oystercatcher (Haematopus ostralegus) ) survived and bred successfully in those areas from which mink were removed each year. These profound effects of mink colonisation show how delicate the balance is between prey and native predators, which has evolved over long periods, and how readily this 544

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balance can be disrupted by the arrival of a non-native predator. The spectacular wholecolony breeding failures caused by mink emphasise how island-breeding, ground-nesting seabirds are unadapted to the presence of ground predators (C. Craik, pers. comm., cf. Erwin et al. 2001). Breeding failures of common gulls in the northern Baltic were also associated with mink predation by Kilpi (1995). When mink numbers declined, production rose signiﬁcantly. When adults were threatened, rapid desertion of colonies might follow. When chicks were taken the desertion process was slower (Kilpi 1995). Linn & Chanin (1978) have highlighted the problem posed by mink predation to seabird colonies of offshore islands. Craik (1997b) noted that mink predation is difﬁcult to detect but should be considered as a possible cause for any sudden decline or disappearance of mainland or near-mainland seabird colonies. Both direct (predation) and indirect (chick parental neglect and exposure) consequences of mink presence in a common tern (Sterna hirundo) colony were reported by Burness & Morris (1993), but Alberico et al. (1991) noted that mink predation on spotted sandpiper (Actitis macularia) nests was signiﬁcantly reduced when a colony of common terns was present. In Georgian salt marshes (USA) mink often made nests of dead Spartina leaves in a hollow tree trunk washed into the marsh on a high tide (Green 1968). Ben-David et al. (1996) studied habitat selection and niche separation by mink and sympatric river otters (Lutra canadensis) living in a coastal environment in Prince William Sound, Alaska. Mink selected shallow vegetated slopes and tidal slopes. They also selected sites that were more protected from wave action and with more understorey cover but avoided beaches with small rocks as the main substratum, which would be likely to be correlated with low availability of food. Niche overlap between the two species (including all habitat variables) was 48%. River otters selected sites with high exposure to wave action, whereas mink showed less preference for such sites. Thus niche separation between the two species was apparent in the Alaskan marine environment but the authors were unable to attribute it to competition. Pronounced resource partitioning occurred even when food was abundant. These ﬁndings contrast somewhat with those of Clode & Macdonald (1995) who investigated evidence for food competition between mink and Eurasian otters on Scottish islands. They found that niche breadth was narrower for otters than for mink. However, niche breadth was wider for both species on islands where they co-existed (cf. allopatric populations). Niche overlap was lower in sympatric populations on islands having mammalian prey. Their data suggested that mink and otter compete for food resources and, when alternative prey sources are available, mink become more generalist predators to avoid competition with otters. In the absence of alternative mammalian prey sources, both species became more generalist.

Polecats Polecats (Mustela putorius), or more likely polecat ferrets (feral ferret x polecat) (note Craik & Brown 1997 regarding difﬁculties of identiﬁcation), have been seen several times at night along the shores of Great Cumbrae Island, Scotland (P. G. M., pers. obs.). Polecats will often build nests in old rabbit burrows, the original occupant having ﬁrst been eaten, and sometimes in rocky crevices (Lawrence & Brown 1967). According to Angus (2001), feral ferrets (M. furo) prefer machair, probably because of the proliﬁc rabbit resources living there. Mustela putorius was nocturnal throughout the year in wetlands in western France, but showed moderate diel activity (31%) (Lode 1995). Goss-Custard et al. (1996) included a 545

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report of a partially eaten oystercatcher (Haematopus ostralegus) having been found in the burrow of a polecat, but whether this had been killed or had been scavenged after death was not clear. Feral ferrets (Mustela furo) may have been responsible for some deaths of sooty shearwaters (Pufﬁnus griseus) in South Island, New Zealand (Lyver 2000). Ferrets are a possible vector of bovine tuberculosis, with a proclivity for denning in outbuildings and a preference for moving along pasture ecotones, for example, fence lines, as shown by Ragg & Moller (2000) in New Zealand (note also Lawrence & Brown 1967). Coastal habitats present markedly linear features.

Stoats The seashore is an atypical habitat for stoats (Mustela erminea hibernica) in Ireland (Sleeman 1993). However, stoats were identiﬁed as responsible for 78% of dead chick and adult sooty shearwaters (Pufﬁnus griseus) in collections of dead bodies made from seven colonies on South Island, New Zealand. Bite marks were concentrated in the head region (Lyver 2000).

Little grison The little grison (Galictis cuja), a South American mustelid, was implicated as the main cause of nest failure during incubation in a South American tern (Sterna hirundinacea) colony studied in Argentina by Blanco et al. (1999).

Bears Polar bears (Ursus maritimus) are important predators of seals, including ringed (Phoca hispida), bearded (Erignathus barbatus) and harp seals (Phoca groenlandica) on land-fast sea ice (Mallinson 1978, Smith 1980, Hammill & Smith 1991). Ringed seals are generally assumed to be the most important and common prey of polar bears but in the ice area of the northern Barents Sea, east of Svalbard an unexpectedly high number of polar bears feed on harp seal carcasses in May and June, explaining the variation in PCB concentrations among bears in the Norwegian Arctic (Kleivane et al. 2000). Polar bears are thought to have evolved from brown bears (Ursus arctos) as a marine mammal predator of seals (primarily) living among the sea-ice landscape (Ferguson et al. 1998). Polar bears (and brown bears, see Pielou 1994, Quinn & Buck 2000) are opportunistic feeders, however, showing behavioural plasticity in response to novel prey items. Brown bears show considerable individuality in behaviour, including their ability to catch salmon in a tidal creek on Admiralty Island (South East Alaska) (Fagen & Fagen 1996). A record exists of brown bear feeding on nestling Steller’s sea eagle (Haliaeetus pelagicus) (McGrady et al. 1999). They have been recorded scavenging marine mammal carcases in Alaska (Peterson 2001). Polar bears only kill seals out of water, even though aquatic stalking is an important hunting technique (Stirling 1974). They creep up on their victims using irregularities in the ice as cover; ﬂattening themselves to the ground if the seal lifts its head (and perhaps hiding its black muzzle with a paw; but see Stirling 1974), then ﬁnally dash forward to deliver a killing blow to the head with a paw. Where the ice offers no features of concealment, Greenlanders have claimed that polar bears push a piece of ice in front of them to camouﬂage their presence (Mallinson 1978). Ringed seals basking on ice are continually vigilant for polar bears; all individuals remaining watchful, not just a sentry. Although there may be 546

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some advantage in having several seals watching for predators around a breathing hole, if a bear stalks near enough to charge, not all seals may be able to get down the breathing hole in time. This may account for why ringed seals are rather antisocial and one breathing hole per seal is the norm. Seal carcasses may be carried up to 2–3 km inland (Stirling 1974). Belugas (Delphinapterus leucas) and narwhals (Monodon monoceros) are vulnerable to attacks by polar bears along the ice edge, in small openings in the ice, or when stranded in tide pools (Smith 1985, Smith & Sjare 1990). They have been reported also taking reindeer (Rangifer tarandus) on Svalbard (Derocher et al. 2000). Hammill & Smith (1991) found evidence of polar bear predation at 18–30% of the ringed seal subnivean structures that they located. Pups made up 75–100% of seals killed. Bears were successful on average in 11.3% of their attempts to kill pups hidden in their birth lairs. Negative correlations were found between mean snow depth and predation by polar bears and between snow depth and number of predation attempts. Polar bear predation success at seal lairs thus decreased as snow depth and roof thickness increased (Furgal et al. 1996). Ringed seal lairs and breathing holes are concentrated in areas of deep snow and associated with large, thick ice ridges. Only a small percentage of the available fast-ice habitat has snow depth sufﬁcient for seal lair construction each year (Furgal et al. 1996). All predators (polar bears, Arctic foxes, Inuit hunters) attacked lairs having the odour of rutting male ringed seals less often than structures with no male odour. Polar bears also feed on seabirds. Stempniewicz (1993) reported Ursus maritimus predation on a little auk (Alle alle) colony on Rubini Rock, Hooker Island, Frans Josef Land. They dug out nests, eating eggs, chicks and adult birds, resulting in decreased breeding success. Donaldson et al. (1995) observed polar bears feeding on thick-billed murres (Uria lomvia) at two breeding colonies (at Coats and Ahpatok Islands, Northwest Territories, Canada) during the summer months. Smith & Hill (1996) presented evidence for depredation of four marked Canada goose (Branta canadensis) nests by polar bear on Akimiski Island, James Bay, Northwest Territories (Canada). Grizzly (or brown) bears (Ursus arctos) will eat ﬁsh. The explorer, John Cabot, encountered American bears rushing into the sea to try to claw cod (Gadus morhua) towards land (Wigan 1998). Brown bears can also annihilate the eggs of whole breeding colonies of geese. They have no objection to carrion, and have been seen out on sea ice eating seal meat that was presumably polar bears’ leftovers. The selectivity of brown bears at times of food glut can be adjudged from their exhibiting a preference for scavenging male sockeye salmon (Oncorhynchus nerka) in Alaskan streams, tending to eat the cranial region (cf. the belly region of ripe females) (Quinn & Buck 2000). A grizzly bear discovered out on the sea ice of Parry Channel had killed seals for itself (Pielou 1994). Black bears (Ursus americanus) are usually active during daylight hours, but animals studied in estuarine and coastal stream habitats on Moresby Island, British Columbia, shifted to crepuscular and nocturnal activity when daylight activities were disrupted, for example, by the presence of brown bears or humans (Reimchen 1998). High foraging success during darkness occurred because salmon showed reduced evasive responses to shoreline disturbance compared with during daylight hours.

Walruses The walrus (Odobenus rosmarus) exists as two sub-species within the Arctic Circle: O. r. rosmarus in the Atlantic and O. r. divergens in the Paciﬁc. Walruses frequent islands, rocky 547

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shores and ice ﬂoes, living mainly on littoral ice (Mallinson 1978). Mixed herds haul out in crowded rookeries. Capable of moving over land at considerable speed, for example, as fast as a man can run (Mallinson 1978), they have even been recorded feeding on seabirds at Coats Island (thick-billed murres, Uria lomvia) by Donaldson et al. (1995). Ovsyanikov et al. (1994) recount a well-documented case of mass death of walruses on a spit of Somnitelnaya Bay (Wrangel Island) when a passing aeroplane panicked them (cf. Kiely & Myers (1998) on helicopter disturbance effects on grey seals) which ﬂew at a height of 800 m above the rookery. Over a hundred animals of all sex and age-classes were killed by being crushed to death. Many traumatised animals able to leave the rookery would die later. Cadavers attracted a range of predators, including polar bears and Arctic foxes. En masse, walruses will threaten polar bears (Stirling 1984), and Gjertz (1990) reported fright behaviour in ringed seal (Phoca hispida) caused by the presence of a walrus. A walrus is a creature that most other marine mammals will avoid due to its great ferocity. Although feeding mostly on benthic molluscs, echinoderms and crustaceans, walruses will crush seals between their front legs, eating them on the shore or on an ice ﬂoe (Mallinson 1978).

Seals and sea lions Fraser Darling (1939) described the effect that Atlantic grey seals (Halichoerus grypus) have on haul-out sites. Their trafﬁc made mud slides of the back-shore environment creating a morass of “stark desolation”. Yet such black bareness is not a mere “seal slum” (Angus 2001), it represents a mass of life and a preparation for the spring burgeoning. The heavy manuring and trampling of the ground makes it ready for the coming year’s greenness of annuals. The vegetation shows close afﬁnities with bird cliff vegetation with abundant oraches (Atriplex spp.), common chickweed (Stellaria media) and sea mayweed (Tripleurospermum maritimum), while the wallows are lined with sea-spurrey (Spergularia sp.), sea pearlwort (Sagina maritima) and toad rush (Juncus bufonis) (Angus 2001). The effect on soil and ﬂora of the increasing grey seal population at the Farne Islands, Northumberland was described by Bonner (1989). In the mid-1950s, when the population was <1000 breeding cows, the seals seemed to have little effect on the vegetation. With increasing numbers of seals, however, there followed progressive destruction of the fragile soil cap that covered these rocky islands. The presence of large numbers of seals prevented or destroyed regrowth and left the soil exposed to erosion. The impact of the seals was additive with the destabilising effects of pufﬁn-burrowing activities and of gulls tugging at vegetation for nest-building purposes. In the UK, the National Trust now has a policy of excluding breeding grey seals (Halichoerus grypus) from the Farne Islands to protect essential seabird breeding habitat. As a result, seals have moved to outer islands and established a major colony on the Isle of May (Pomeroy et al. 2000, P. Hammond, pers. comm.). Bonner (1985) described the impact of Antarctic fur seals (Arctocephalus gazella) on the vegetation of Bird Island, South Georgia. Profound changes have taken place there since 1966. The expanding population of fur seals has destroyed the tussac grass, which is the dominant plant cover, by trampling and lying on the tops of the tussocks. Destruction of the tussac deprives birds like pipits of feeding and nesting habitat, and petrels of burrowing habitat. Trampling causes burrows to collapse, destroying nests and exposing smaller birds to skua predation. Cave beaches are possible grey seal breeding sites, but Williamson & Boyd (1960) speculated from their experience at St Kilda (the most exposed of the Outer Hebrides) that swell there would make these very dangerous and were doubtful if any were tenable for 548

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calf-rearing. Untenability might not prevent seals from trying to rear young in caves, but they speculated that if they did then casualties would be bound to be high (see p. 510). Grey seals are now much less numerous on North Rona, where being washed away in gullies was a signiﬁcant cause of mortality (Summers et al. 1975; S. Angus, pers. comm.). Stillbirth was reported by Davies (1949) as the most important mortality factor among grey seals on Ramsey Island, south Wales, accounting for up to one-third of calf deaths. Starvation can also be a major cause of pup mortality, brought about by failure of the mother-pup bond (Fogden 1971, Anderson et al. 1979), and may be density-dependent. Overcrowding on narrow beaches can become extreme at high tide (Anderson et al. 1979). Pup mortality was related to numbers per metre of accessible shoreline in grey seals on the Farne Islands (northeast England). The further the breeding site was from the water’s edge in dense seal colonies, the greater the distance that pups had to travel through hostile territories of other seals (Boness et al. 1982), especially through regularly used gullies and hollows, to reach the sea (Coulson & Hickling 1964). Emaciation was commonly associated with strandings of Guadalupe fur seals (Arctocephalus townsendi) on Californian coasts, associated with El Niño events (Hanni et al. 1997). Since 1989, the colony of Hawaiian monk seals (Monachus schauinslandi) on French Frigate Shoals, North Paciﬁc Ocean (which in the mid-1980s had represented half the world’s population of this species), had declined by 55%, primarily due to poor juvenile survival. Emaciation and slower growth of juvenile seals (inter alia) pointed to reduced prey availability as causing the decline, which is still continuing (Craig & Ragen 1999). Wounding of this species as recorded on Laysan Island and French Frigate Shoals resulted from male mating attempts, non-mating aggressive interactions between seals, attacks by sharks, contact with coral reef or debris, and netting entanglement (Hiruki et al. 1993a). In a mass mortality event in spring 1997, involving the largest aggregation of the highly endangered Mediterranean monk seal (Monachus monachus) located at Cap Blanc Peninsula (western Sahara), however, it was adults which were most affected (Forcada et al. 1999). Southern elephant seals (Mirounga leonina) hauling out among the tussac grass in late summer to moult spend much of their time in large “pods” often totally submerged (nostrils excluded) in foetid wallows on South Georgia (Headland 1984, Fothergill 1993) (Fig. 8). These wallows are organically enriched both with their own excrement (Strange 1992), with moulted hair and occasionally dead bodies of seals which have trespassed into wallows too steep-sided and slippery from which to extricate themselves (Fedak et al. 1994, P. G. M. pers. obs.). A nitrogen-ﬁxing Clostridium sp. was isolated only from soil in the vicinity of a seal wallow on the coast of Macquarie Island by Line (1992). Moulting lasts some 32 (S.D. ± 6.6) days during which time approximately 3.5% of total mass loss was associated with the shedding of the pelage and epidermis (Worthy et al. 1992). At Port Lockroy in the Southern Ocean, Cobley & Bell (1998) reported attempts by a Weddell seal (Leptonychotes weddellii) to ambush gentoo penguin chicks (Pygoscelis papua) when they were wading in the shallows. The seal was observed to drift towards a group of chicks until it was grounded and then to suddenly launch itself at them, hauling rapidly up the beach until almost out of the water. The frequency of the attacks increased as the penguin chicks spent more time at the water’s edge, and in March ambushes were observed most days. The penguins reacted to these attacks by moving well back from the edge of the water and avoiding entering the sea; behaviour that sometimes persisted for several hours after the departure of the seal. California sea lions (Zalophus californianus) have been identiﬁed as a predator of common murre (Uria aalgae) chicks along the west coast of North 549

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Figure 8 (top and bottom) Elephant seal (Mirounga leonina) wallows in immediate backbeach area, Stromness whaling station, South Georgia, Southern Ocean (Photos: P. G. Moore).

550

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America (Long & Gilbert 1997). Sea otters were fearful of a California sea lion but were unconcerned by the presence of a harbour seal (Phoca vitulina) (Limbaugh 1961). New Zealand (or Hooker’s) sea lions (Phocarctos hookeri) have been described consuming New Zealand fur seal (Arctocephalus forsteri) pups on the Otago peninsula, New Zealand. The evidence from three sea lion regurgitations suggested that consumption was due to predation not scavenging (Bradshaw et al. 1998; see also Robinson et al. 1999 at Macquarie Island). The hunting behaviour of normally solitary leopard seals (Hydrurga leptonyx) (Headland 1984) was monitored opportunistically on beaches by Hiruki et al. (1999) at Seal Island, South Shetland Islands, Antarctica. Juvenile leopard seals fed mainly on krill, but older seals fed on penguins, seals and other prey. Penguins formed an especially large proportion of the leopard seal diet in January to March, when the birds were ashore for breeding and readily available to the leopard seals. Few observations exist of leopard seals hunting pinniped prey due to the logistical difﬁculties of observing animals in the pack ice. Leopard seals used several methods to catch Antarctic fur seal pups (Arctocephalus gazella) and chinstrap penguins (Pygoscelis antarctica) at Seal Island, with individuals showing different hunting styles and hunting success. One or two leopard seals per year were responsible for an average of 60% of the observed captures of fur seal pups. Leopard seals preyed on penguins throughout the summer, but preyed on naïve fur seal pups during a 2 month window of opportunity, between late December and mid-February. Hunting behaviour differed signiﬁcantly between different locations on the island; fur seals were hunted only at one colony while penguins were hunted in several areas. These authors speculated that hunting success of leopard seals pursuing these prey species is probably high enough for co-operative hunting not to become a common hunting strategy. Subantarctic fur seals (Arctocephalus tropicalis), which were born early on Amsterdam Island, South Indian Ocean, were often stillborn (Georges & Guinet 2000). The average mortality rate from birth to age 30 days in South African fur seals (A. pusillus pusillus) was 20% at Atlas Bay, Namibia (De Villiers & Roux 1992). Death of grey seal (Halichoerus grypus) pups on the Isle of May (Scotland) (average 12.5% in 1986) was caused mainly by adult–pup interactions (Baker & Baker 1988). A wide and differing sex ratio of dead pups was found on different beaches on the Isle of May, however, which awaits explanation (Baker & Baker 1988). The infant mortality rate of grey seals on Ramsey Island, Pembrokeshire (Wales) was estimated to be about 15% on open beaches (20% in caves) (Davies 1949). According to Harrison Matthews (1982) pup mortality for grey seals (H. grypus) is between 12–50% depending on degree of overcrowding in the colony. Pups starve after becoming separated from their mothers. In extremis, a starveling pup will attempt to suckle from strange cows, other pups, even from itself, or rocks or bits of seaweed (Fogden 1971). Blood lost by seals during parturition (or agonistic encounters) will contribute small-scale organic input to beach sediments, although scavenging seabirds, for example, the two sibling species of vulture-like giant petrels, Macronectes giganteus and M. hallii, on South Georgia (sub Antarctica) or jackals and hyenas at places in Namibia (see references above), soon remove most visible traces of placental tissues from elephant seal pupping sites (cf. Davies 1949 regarding grey seals). It is the male giant petrels which mainly scavenge on the coast of South Georgia (cf. females that forage mainly at sea) attending seal carcasses, their numbers showing a strong dependence on Antarctic fur seals during the brooding and chickrearing season (González-Solís et al. 2000a,b). Dolphin gulls (Larus (Leucophaeus) scoresbii) were associated with colonies of southern sea lions (Otaria ﬂavescens) in the Islas Bridges 551

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zone of the Beagle Channel (Tierra del Fuego, Argentina) (Rey & Schiavini 2000). This gull species is a specialised scavenger, feeding on southern sea lion excrement and food scraps dropped by colonially-nesting seabirds (Yorio et al. 1996). On the breeding colonies, adult female grey seals frequently attempt to attack greater black-backed gulls (Larus marinus) which mob new-born pups and particularly target the afterbirths (S. D. Twiss, pers. comm.). Often, the female seals succeed in injuring the less cautious gulls and each breeding season gulls are observed with broken wings, presumably resulting from such encounters. Early mortality in the South American fur seal (Arctocephalus australis) in Peru was considerably higher than that seen in any other population of fur seal; 31– 49% in the ﬁrst month as opposed to a maximum of 20% in other populations. In 1987 and 1988, pup mortality was enhanced by density-related effects and predation on pups by the southern sea lion (as Otaria byronia = O. ﬂavescens see Rodriguez & Bastida 1993) (Harcourt 1992). Localised impacts of seals will be caused by microhabitat selection at haul-out sites and habitat rearrangement for thermoregulatory purposes. Both immediately before and after parturition, female grey seals often dig themselves into shingle with their forelimbs, creating quite large depressions (Davies 1949). Environmental factors inﬂuenced the deaths of newborn South African fur seal (Arctocephalus pusillus pusillus) pups at mainland colonies in Namibia, a combination of changes in wind velocity and direction leading to heat stress in pups being the most important (De Villiers & Roux 1992). The northern elephant seal (Mirounga angustirostris) cannot cope physiologically with too high temperatures on land and will ultimately escape to sea. Before that point is reached, however, it will lie in the surf or ﬂip wet sand over its body, seek shade or remain very inactive. Hawaiian monk seals (Monachus schauinslandi) are as inactive as possible while on land and do not spend energy in ﬂipping sand or waving ﬂippers or even in much social behaviour. They spend the night high up the beach on dry sand and move towards the sea so that they spend the hot day on wet sand or in the surf. Southern elephant seals (Mirounga leonina) also throw sand onto their backs with their ﬂippers. This activity may be partly thermoregulatory in this species, but as it happens most when the humidity of the air is low, it probably also alleviates irritation of the skin caused by drying (King 1983). There is some speculation that a skin disease of young (<2 yr) northern elephant seals, which results ultimately in massive skin necrosis (note also Gerber et al. 1993), may be due to PCB toxicosis (Beckmen et al. 1997). Temperature regulation accounts for microhabitat choice by free-ranging Galápagos fur seal (Arctocephalus galapagoensis) pups (Limberger et al. 1986). Social behaviour of cold-water adapted otariids (fur seals and sea lions) in hotter climes is constrained by the form and availability of cool substratum provided by the rookery environment. Female Juan Fernandez fur seals (Arctocephalus philippii) on Alexander Selkirk Island, Chile, made daily movements from inland pupping and rest sites to the shoreline and into the water in response to rapid increases in solar radiation. Thirty per cent of these females ﬂoated and groomed offshore in the afternoon in areas protected from the surf by offshore islets and rocky reefs. Atypically for male fur seals, 16% held aquatic (rather than terrestrial) territories that encompassed the site where females ﬂoated (Francis & Boness 1991). Pup mortality in South American fur seals (A. australis) in Peru was linked with female aggression in high-density colonies and to movement within the colony to thermoregulate (Harcourt 1992). The haulout behaviour of leopard seals (Hydrurga leptonyx) was negatively related to wind-chill index in Prydz Bay, Antarctica (Rogers & Bryden 1997). Interactions of seals with other species may also warrant consideration. Thus goose barnacles (Lepas spp., Conchoderma auritum) and green, brown and red algae attaching 552

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themselves to vibrissae or guard hairs of seals (King 1983), die when seals haul out during the breeding season. Also, the high incidence of wounding (Fothergill 1993) inﬂicted by male seals during courtship and territorial defence, male crushing of pups, and female seals biting another’s pup, generate wounds that may become infected with a variety of bacteria (Keyes 1965). Pups are immunologically deﬁcient (Baker 1984). The vast majority (87.5%) of adult female Hawaiian monk seals (Monachus schauinslandi) that died on Laysan Island, northwestern Hawaiian Islands, in 1983–89 sustained injuries from adult male seals (Hiruki et al. 1993b). Among Scottish grey seal (Halichoerus grypus) pups, starvation and infection were the major causes of mortality on on the island of North Rona, but on the Monach Isles, starvation was a principal source of loss (Baker 1984). Even cannibalism has been reported in this species (Kovacs et al. 1996). Leptospirosis also causes a high mortality in seals (Lander 1979). Thus the presence of rats in seal colonies, as on many elephant and fur seal breeding beaches in the vicinity of abandoned whaling stations in the Southern Ocean, may encourage spread of this disease. Gulland et al. (1996) found clinical signs of renal disease caused by leptospirosis in 33% of California sea lions (Zalophus californianus) stranded live along the central California coast between January 1981 and December 1994. Of that 33%, 71% subsequently died. Epizootics of leptospirosis occurred in 1984, 1988, 1991 and 1994, and were more common in the autumn, typically affecting juvenile males. Other infections though, like orange fungal spots on some southern elephant seals, present when coming from the sea, dry up on land (King 1983). For a review of bacterial and fungal infections of marine mammals, see Higgins (2000). Microbes from sea water (Vibrio, Aeromonas and Pseudomonas spp.) can contaminate marine wounds in mammals, sometimes fatally (Kueh et al. 1992). Bites from adult seals are rarely fatal but give rise to septic wounds (Coulson & Hickling 1964). Bacteria involved were opportunistic invaders. The unregulated expansion of now iconic Californian sea lion (Zalophus californianus) populations has led to the closure of shellﬁsh beds poisoned by faecal contamination from the sea lions (Wigan 1998). Underweight and emaciated pups and neonates dominated the 1446 stranded pinnipeds of six species recovered along the coast of central and northern California between 1984 to 1990. They were afﬂicted by a variety of diseases including renal disease, verminous pneumonia, seizures of unknown etiology and haemorrhagic enteritis (Gerber et al. 1993). A total of 244 pinnipeds of six species were recorded as sick, dead or injured from strandings data collated from Western Australia over the period 1980–96 by Mawson & Coughran (1999). The most commonly encountered species (61%) was the Australian sea lion (Neophoca cinerea), and of these 28.5% died as a direct or indirect result of interaction with humans. The most common cause of unnatural death was gunshot wounds and the most common cause of natural death was respiratory failure. Over 400 Californian sea lions (Zalophus californianus) died, and many others displayed signs of neurological dysfunction, along the central Californian coast during May and June 1998. A bloom of the diatom Pseudonitzschia australis was observed during the same period. Domoic acid (DA), a neurotoxin associated with the bloom, was found in sea lion body ﬂuids and in ﬁshes but not in monitored mussels (Scholin et al. 2000).

Sirenia Relatives of the elephant (itself quite an accomplished aquanaut (Coukell 2001; see also p. 000), manatees and dugongs are vegetarians, living on seaweed, seagrasses or other 553

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alongshore vegetation. Dugongs have a more marine habit than manatee (Heinsohn 1986). One manatee species (Trichechus senegalensis) lives on the west coast of Africa from Senegal to around the Cape of Good Hope, another (T. manatus) lives along the western Atlantic coast of the Americas from Beaufort, N.C. to the Orinoco River, being more abundant along the shores of the Caribbean (MacGinitie & MacGinitie 1968). There is very little literature on the coastal ecology of manatees and their impacts compared with their relative, the dugong (see below). Food availability is a strong factor inﬂuencing distribution (Anderson 1998, Axis-Arroyo et al. 1998). Such large, non-ruminant herbivores require considerable food intake. Manatees consume c. 8% of their body weight in aquatic plants daily, whereas the same value for the dugong is about 14%. Digestibilities of aquatic plants vary from 45–70% for manatees, and a single in vitro measurement for dugong was 83% (for seagrass) (Best 1981). Only the molar teeth of the manatee erupt and are functional (Harrison & King 1965). Captive animals have been seen “walking” in shallow water on the inturned tips of their ﬂippers but they are quite incapable of locomotion when completely stranded. Indeed their weight makes it impossible for them to breathe properly when out of water (Harrison & King 1965). Lefebvre & Powell (1990) noted that manatees are among the few large vertebrate grazers in seagrass systems. They showed that manatee grazing signiﬁcantly reduced both aboveand below-ground biomass of the dominant seagrasses, Halodule wrightii and Syringodium ﬁliforme, at 16 feeding locations on the east coast of Florida. It is depressing to note that Beck & Barros (1991) have highlighted the problems posed to the Florida manatee (T. m. latirostris) from ingestion of debris (ﬁlamentous ﬁshing line, plastic bags, string, ﬁsh hooks, etc.) while feeding. Morales-Vela et al. (2000) have described the problems posed to manatee populations by ecotourism. The dugong (Dugong dugon) lives (perhaps to 50 yr; Branson & Branson 2000) in the IndoPaciﬁc, from the east coast of Africa to the Malaysian archipelago (Harrison & King 1965), including the Great Barrier Reef; this being one qualiﬁcation for its World Heritage status (Anon. 2000) ). Following the extermination of dugongs in many Indian Ocean locations, Australia is now the species’ main stronghold (Preen 1998). Dugongs scratch-off vegetation using large bristles and stiff hairs around the lower part of the muzzle (Harrison & King 1965). Digesta passage times were measured by Lanyon & Marsh (1995) in two captive dugongs and were found to be 146–166 h (i.e. much longer than most other herbivorous mammals), although comparable with the West Indian manatee. Such a slow gut passage time may be explained by the dugong’s long digestive tract, the low ﬁbre content of the diet and low food intake. Low ﬁbre material is retained for extended periods within the long hindgut and digested almost completely. In Moreton Bay, Australia, dugongs often graze in large herds at the same location for weeks or months, substantially reducing seagrass shoot density. Following even the most intense and sustained grazing, the space between surviving tufts of seagrass remains small (<1 m2) and recovery is usually rapid (months) although recovery can be suppressed by low levels of sustained grazing pressure. The species composition of seagrass meadows can be altered by intensive grazing, which favours rapidly growing, early pioneer species, such as Halophila ovalis, at the expense of slower growing but dominant species such as Zostera capricorni. By preventing the expansion of Z. capricorni and increasing the abundance of Halophila ovalis, so-called “cultivation grazing” can improve the quality of the dugong’s diet (Preen 1995a, Bowen 1997). 554

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Similar rapid recovery (<20 day) of seagrass beds (H. ovalis) after disturbance by dugong herbivory in Thailand was reported by Nakaoka & Aioi (1999). These authors found that net production, leaf production and rhizome elongation rates were 2–3 times greater at the patch edge than at its centre, indicating plasticity in growth of the seagrass in response to dugong grazing. Anderson (1998) noted that dugongs appeared in numbers in a monospeciﬁc meadow of Halodule uninervis at Shark Bay (Western Australia) in summer, coincident with peak rhizome productivity, and foraged exclusively by rooting into the substratum. Because Halodule leaves are fragile and much leaf material is lost to drift, dugongs foraged effectively only for rhizomes and selected a diet rich in readily digestible carbohydrates (cf. comments above on North Ronaldsay sheep, p. 559). Peterken & Conacher (1997) reported observations made on a seagrass meadow dominated by Zostera capricorni in Australia that was intensively grazed by dugongs, leaving a bare sand surface. In less than a year densities had returned to pre-grazing levels. This recolonisation seemed to be facilitated by enhanced sexual reproduction by the seagrass, because seed production at the study site was considerably higher than had been found in previous studies of Z. capricorni. Seagrass distribution and seasonal changes in biomass and total organic carbon (org.-C) were studied in the Moluccas (East Indies) by Deiongh et al. (1995). They found that dugong grazing removed 93% of the shoots and 75% of the below-ground biomass of the upper 4 cm of sediment. Seagrass biomass was restored to levels of the nearby seagrass bed after 5 months during the onset of the wet season. No signiﬁcant restoration took place during the dry season. The frequency of dugong grazing showed a strong positive correlation with total org.-C concentration in the below-ground plant tissues, indicating that the dugong’s preference for Halodule uninervis was based on a strategy of a high net rate of energy intake. The integrity of seagrass patches is achieved by shoot recruitment matching shoot mortality rates (Duarte & Sand-Jensen 1990). Recovery times for 11 bare circular areas (“holes”) within a Posidonia australis bed in southeastern Australia (created by seismic surveying in the late 1960s) were found by Meehan & West (2000) to be slow but consistent, with mean spreading rate estimated to be 21 ± 2 cm yr−1 over a 25 yr period. Damaged seagrass beds can be artiﬁcially enhanced but Fonseca et al. (1996) noted that conservation of existing seagrass habitat provides a more certain basis for maintaining the resource than attempting to mitigate losses through replanting. Catastrophic habitat loss of up to 1000 km2 of seagrass beds in August 1988 in southern Hervey Bay, Queensland following two ﬂoods and a cyclone, resulted in the local dugong population crashing from 2206 (S.E. ± 420) to 71 (S.E. ± 40). Recovery of that dugong population was anticipated to take 25 yr (Preen & Marsh 1995). The impact of food loss on megafauna with K-selection will thus be considerable and long-lasting. Both the grazer and the grazed resource are of high conservation value. The dugong is now listed under Appendix I of CITES (Anon. 2000) so an international ban on commercial trade in this species, including its meat (“sea-pig”, a Muslim delicacy; see Harrison & King 1965), should help conserve stocks. Strategies for integrated management of grazing by dugong herds were advocated by Baldwin (1988). Interestingly, Preen (1995b) discovered that ascidians were an important dietary component in Moreton Bay, Australia. They occurred in 73% of 48 faecal samples examined, and accounted for 26% of their weight. Dugongs fed deliberately on both small stalked colonial and large solitary ascidians. One even fed selectively on a gregarious polychaete. Preen suggested that nutritional stress caused by seasonality in abundance of seagrasses may explain the omnivory of the Moreton Bay animals, which live at the southern edge of the dugong’s range. 555

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History reveals just how easily sirenians can be exterminated. Steller’s sea cow (Hydrodamalis gigas), after its discovery in the Arctic waters of the Bering Strait in 1741, was hunted to extinction by 1768 after only 27 yr of intensive exploitation. A huge animal (weighing ∼4.5 t) bereft of teeth and masticating its food using keratinous rostral pads (see illustration in Haley 1978), it seems it fed mostly by ripping off brown and red algae (kelps especially). Seagrass was only a minor component of its diet (Domning 1976, 1978, Anderson 1995). Living sirenians are known to ingest brown algae in times of food shortage, but seagrasses are their preferred food. Clearly, living so far north, seasonal food availability may have been a problem in the Bering Sea and Steller (1751) described how individuals lost enough weight during the winter months to cause ribs and vertebrae to be visible under the skin. Seagulls were known to perch on their backs, which were usually half out of the water, to feed on parasitic amphipods (Cyamus rhytina) (Haley 1978). Anderson (1995) noted that the intense hunting of sea otters on the Bering Sea islands may have contributed to the ﬁnal extinction of Hydrodamalis gigas. They were killed for food by Russian hunters seeking sea otter pelts. It is known that sea urchin populations can severely deplete seagrass and kelp beds when otters are removed (see p. 541), and as this happened on the Bering Sea islands, the sea cows would also have faced competition for food. At present, the sea cow is generally accepted as being the only North Paciﬁc sea mammal to have become extinct in historical times. Two documented Soviet sightings (1962, 1977) of very large unusual-looking animals near Cape Navarin have been made in more recent times, however, and may have been this species (Haley 1978).

Perissodactyla In their native Scottish islands, Shetland ponies (Equus caballus) will eat seaweed (Edlin 1952). Saltmarsh grazing by horses was referred to by Ranwell (1972). Horse grazing had a substantial impact on standing stocks and net above-ground primary production of Spartina in saltmarsh habitats on Cumberland Island National Seashore, Georgia (USA), but grazing was not uniform throughout the marsh (Turner 1987). Evidence of selective grazing upon Spartina alterniﬂora by horses was found in saltmarsh vegetation on Assateague Island, one of the Virginia barrier islands, by Furbish & Albano (1994) from examination of grazing signs, observation of horse behaviour and examination of horse dung for grass epidermal fragments. Horse grazing at this locality was regarded by Keiper (1990) as having caused gradual deterioration of dune habitat after the horse population more than doubled there between 1975–90. Using the rate of tail swishing as a crude index of insect harassment, Keiper & Berger (1982) showed that, among feral horses from an insular mountain range in the deserts of Nevada and a barrier island off the Atlantic coast of Virginia and Maryland, this behaviour was least at beach and bay sites. These sites, in addition to the sea itself, were used as refuges from insect harassment mostly during the summer, and particularly between 1000 h and 1600 h. (Note similar comments relating to red deer and caribou, below.) Sterile horse-hair baits incubated on moist sand from a Majorcan beach yielded a profusely fruiting keratinophilic gasteromycete fungus new to science (Nia epidermoidea). Hair baits submerged in the sea nearby also yielded fruiting bodies of this species after moist incubation (Rossello et al. 1993). Hair baits from buffalo, cattle and sheep incubated with saltmarsh soils from the Nile delta yielded 44 species of keratinolytic fungi at 37°C (Moubahser et al. 556

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1992). Horsehair has traditionally been used by native peoples in many cultures to make ﬁshing lines (Wigan 1998). The native St Kildans used to use multiple small horsehair nooses to catch pufﬁns by entangling their ungainly feet (Steel 1975).

Artiodactyla Sheep Hercus (1929 cited by Tribe & Tribe 1949) reported that sheep (Ovis aries) in South Otago, New Zealand showed “a remarkable craving for seaweed”, and suggested that this may be attributed to an iodine deﬁciency in their normal diet (note recent work by Parker & McCutcheon 1989, Clark et al. 1998, Sargison et al. 1998). In the UK, North Ronaldsay in the Orkney Isles is famous for its exclusively seaweed-eating sheep (Fig. 9). These sheep are retained on the seaward side of a wall (the Sheep Dyke) that surrounds the island (Fig. 10). The plants outside this dyke are stunted as a result of exposure and heavy grazing pressure. Feeding on seaweeds, notably Laminaria digitata, means that the sheep are never short of food. Tribe & Tribe (1949) reckoned that these are probably the only sheep in Scotland that were better fed in winter than in summer (but note Feldmann et al. 2000). The North Ronaldsay sheep preferred to eat the meristematic tissue at the junction between the stipe and the frond, proceeding then to consume the complete frond and, after that, to gnaw the stipes (Tribe & Tribe 1949). These authors noted that other Laminaria species were equally acceptable (see also Conway 1942) but that the Fucales were generally unpalatable. Phaeophyta (brown algae) possess high concentrations of polyphenolic compounds

Figure 9 Sheep (Ovis aries) grazing on seaweeds on the island of North Ronaldsay, Orkney Isles (Photo: R. Welsby).

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Figure 10 The island of North Ronaldsay, the northernmost of the Orkney Islands (UK). Map shows the position of the Sheep Dyke (wall) and sheepgrazed area (solid), freshwater lochans and wet meadow areas (inside hatched perimeters), rocky shore (cross-hatched) and sandy shore areas (stippled). The typical shore proﬁle below shows the position of the perimeter wall that excludes sheep from the centre of the island. Arrows demarcate different littoral algal communities, as follows: A = Fucus and Ascophyllum zone, B = Laminaria digitata, L. saccharina, Palmaria palmata, Alaria esculenta zone, C = Laminaria hyperborea forest (Shore proﬁle modiﬁed after Paterson, 1987).

(phlorotannins) and other active molecules (terpenes, lactones) that could inﬂuence dietary choice, as has been found with invertebrate grazers (Steinberg & Vanaltena 1992). Other brown algae (e.g. Himanthalia lorea, Halidrys siliquosa and Chorda ﬁlum), were uncommonly eaten by these sheep (Tribe & Tribe 1949, Paterson & Coleman 1982). Among the 558

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red seaweeds, dulse (Palmaria palmata), Chondrus crispus and Mastocarpus (= Gigartina) stellatus, were the most palatable, the dulse being eagerly nibbled off the stipes of cast kelp. Tribe & Tribe (1949) note, however, that the sheep never put their noses into sea water even to reach palatable red algae in rock pools. They point out that the preferences for different seaweeds largely reﬂect nutritional value, and that the sheep must derive useful minerals (like calcium) from ingestion of calcareous tubeworms attached to the kelp. The rumen microbiology of seaweed digestion in Orkney sheep has been studied by Greenwood et al. (1983) and Orpin et al. (1985). Competitive resource sharing by the seaweed-eating sheep on North Ronaldsay was reported by Paterson (1987). In summer, sheep ate Laminaria species (Paterson & Coleman 1982) and Laminaria species were the most common species, yet sheep selected the less abundant Palmaria (= Rhodymenia) palmata and Alaria esculenta. Although both A. esculenta and Palmaria palmata were available in autumn, only P. palmata was selected since Alaria esculenta plants were then necrotic and covered in epiphytes. New growth of A. esculenta was selected in spring. The sex ratio of sheep foraging in particular habitat patches was signiﬁcantly different from the beach as a whole, being related to the distribution of dominant rams (Paterson 1987). The main feeding period was in the 4 h preceding low tide, and sheep supplemented their diet with rough grazing, if available, when the tide covered the algae (Paterson & Coleman 1982). The energetic cost of reproduction has been shown to be a major cause of mortality in adult ram Soay sheep on the island of Hirta, St Kilda (Jewell 1997). This population crashes every 3–5 yr with up to 60% mortality. Inducible fungal endophytes of red fescue grass, Festuca rubra, from Hirta were found by Bazely et al. (1997) to be present in 65–100% of shoots. This frequency was positively correlated with grazing pressure. It was signiﬁcantly lower on the neighbouring ungrazed island of Dun, supporting the hypothesis that endophytes deter herbivory. Interestingly, there were no signiﬁcant differences in endophyte content between grazed and ungrazed populations of F. rubra from the hebridean islands of Rum and Benbecula. Fungal endophytes of tall fescue, F. arundinacea, have been found to synthesise toxic alkaloids which deter herbivores and cause widespread losses to the US livestock industry (Bazely et al. 1997). Sheep have been reported by Furness (1988) to amputate limbs from tern (Sterna spp.) and arctic skua (Stercorarius parasiticus) chicks on Foula (outer Hebrides) and to beneﬁt from their mineral content in this mineral-deﬁcient Highland location.

Cattle The inﬂuence of grazing by water buffalo (Bubalus bubalis) and cattle (Bos taurus) was studied on vegetation inside and outside cages in a saline area at Khon Kaen, northeast Thailand by Nemoto & Panchaban (1991). Livestock preferred the perennial grass Panicum repens. Grazing led to an increase of bare ground where salt accumulated and modiﬁed the heterogeneity of the vegetation. Feral cattle have been reported by Hall & Moore (1986) foraging on seaweed (Laminaria fronds and stipes, large pieces of Fucus serratus) on Swona, Orkney Islands, but only to a minor, and less than anticipated, extent. Cattle fed at the strand line and did not venture between the tidemarks. It was clear that they found moving across slippery, loose rocks uncomfortable. Observations with an image intensiﬁer showed that little foraging was done at night. This is in sharp contrast to the behaviour of the North Ronaldsay sheep (see p. 557) which prefer seaweed with the highest nutritional value 559

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(Paterson 1985) and show a close correspondence between feeding behaviour and state of the tide, feeding at night if necessary (Hall 1975, Paterson & Coleman 1982) and ranging between the tidemarks. On Devon Island, Northwest Territories, in the Canadian High Arctic, muskox (Ovibos moschatus) habitat is restricted to small, spatially isolated, islands of productive sedge and moss meadow (3% of land area) within a matrix of sparsely vegetated polar desert. Muskox herds move along the northeast coast adjacent to Jones Sound in response to the seasonal availability of sedge-dominated habitat (Pearce, 1991).

Pigs From their researches on the social organisation of wild boar (Sus scrofa) in the coastal Doñana National Park (Spain), Fernández-Llario et al. (1996) noted that there were differences in habitat use between family groups. Groups without piglets tended to use scrub on mobile dunes less and the ecotone (that area between the shrub and the marsh) more than groups with piglets. The overall use of habitats by groups of both types was 11.99% (marsh area), 38.36% (ecotone), 33.49% (shrub on mobile dunes) and 16.16% (shrub on ﬁxed dunes). During the farrowing period (late winter and early spring) the shrubs on mobile dunes represent an important food source (i.e. when pines (Pinus pinea) release their mature seeds and many wild boars collect there). Winter food availability has been shown to be a key issue affecting home range, activity and reproductive success of wild boar in a Mediterranean coastal area, where high-energy foods like acorns, olives and pine seeds contribute most to bodily condition (Massei et al. 1996, 1997). Bearded pigs (Sus barbatus) account for some of the mortality of turtle hatchlings experienced on beaches in Sarawak (Hazebroek & Abang Kashim bin Abang Morshidi 2001).

Goats The Punihuil Islands, off the coast of Chiloe (S. Chile) support the only known mixed colony of Humboldt (Spheniscus humboldti) and Magellanic penguins (S. magellanicus). On the island closest to shore and most frequently visited by tourists, 28% of dirt burrows have collapsed, mainly by accidental trampling by humans and the activity of introduced goats (Capra hircus). In addition, goats graze the vegetation used by penguins to build their nests. On the island furthest offshore, with no goats and fewer tourists, only 10% of dirt burrows had collapsed (Simeone & Schlatter 1998). Feral goats are very selective in what they eat, but have the ability to switch their diets as more palatable food species are eliminated from their habitat (Parkes 1993). Many mountain goats (Oreamnos americanus) were reported by Demarchi et al. (2000) in timbered areas near the coast in British Columbia, Canada.

Deer Coastal red deer (Cervus elephas) in the Highlands and Islands of Scotland may resort to eating seaweed, mostly Laminaria spp. (Fig. 11), during the winter (Vesey-Fitzgerald 1946, Mallinson 1978, Whitehead 1993, Wigan 1993), particularly when higher altitude grazing is snow girt (see also Peterson 2001 on sitka black-tailed deer Odocoileus hemionus sitkensis in Alaska). According to Clutton-Brock & Albon (1989), on the Island of Rum (Hebrides), Laminaria species may account for as much as 10% of the total diet in some areas between 560

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Figure 11 Red deer hind (Cervus elephas) feeding on seaweed, Isle of Mull, Scotland (Photo: Fiona Guinness).

November and February. The use of seaweed habitat by red deer on Rum has been examined in detail recently by Conradt (2000). He showed (inter alia) that seaweed use by adult males and females was closely correlated to that of their mothers, implying that deer learn early in life to include seaweed in their diet. All deer fed on the stranded and growing fractions of available seaweed and usually ate from both fractions in a single session. Interestingly, males and females used different bays and within bays they used different fractions of seaweed. However, male-preferred sites did not yield higher intake rates and were not of lower forage quality than sites preferred by females, suggesting that the “indirect competition hypothesis” (involving male inferiority in dimorphic ungulates) could not explain the observed pattern of site segregation. Sitka black-tailed deer will also consume drift algae and Fucus on the shore (Peterson 2001). By contrast to exploiting rocky shores in winter, in summer red deer may congregate on sandy beaches to rest and gain respite from the hordes of ﬂies inland (see illustrations in Wigan (1993) and cf. Keiper & Berger (1982) on horses). They may graze on precipitous sea-cliff ledges (Wigan 1993), and Furness (1988) found red deer biting the heads off young Manx shearwaters (Pufﬁnus pufﬁnus) and chewing shearwaters’ legs and wings to excise bone, on the mineral-deﬁcient island of Rum. Malnutrition in winter often drives deer from preferred habitats to forage elsewhere as best they can (Takatsuki et al. 1994). C. Craik (pers. comm.) reports ﬁnding red deer skeletons most years on small grassy islets (most c. 300 × 100 m) in Loch nan Uamh, West Scotland, suggesting that they face harsh winter conditions on such islets, which are often very trampled and overgrazed by deer (no sheep or cattle). The hebridean islands of Taransay and Pabbay are both heavily grazed by stock, and the red deer undoubtedly contribute to the very high grazing pressure on the machair of both islands, probably contributing to erosion on Pabbay and releasing sediment from the higher 561

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machair which is washed down, smothering some of the valued machair ﬂushes on the low ground (Angus 2001). The huemul deer (Hippocamelus bisulcus) is an endangered species (Frid 1999). Springtime habitat use (inter alia) by a coastal population of this species was investigated in a Chilean periglacial fjord by Frid (1994). Adult males and juveniles primarily used periglacial grassland, while adult females used mainly bluff habitat. Newborn fawns were seen only on bluffs. Adult females may be using bluffs during spring to minimise predation on their newborn offspring. Coastal populations have suffered less human impact than interior populations although some coastal populations have been affected by logging. For this reason, Frid (1994) argued for more research on coastal populations before decisions were made to transplant coastal huemul deer to interior reserves. Wehausen & Elliott (1982) found that coastal populations of axis (Axis axis axis) and fallow deer (Dama dama dama) on Point Reyes Peninsula (California) beneﬁted from vegetative changes associated with livestock grazing. Both these species preferred areas used by livestock. Introduced populations of Philippine deer (Cervus mariannus) occur on four islands in Micronesia: Guam, Rota and Saipan in the Marianna Islands, and Pohnpei in the Caroline Islands. Currently, deer abundance appears to be expanding on Guam (Wiles et al. 1999).

Caribou/reindeer Harassment of female caribou (Rangifer tarandus) by insects may negatively affect the cow’s energy balance during the critical post-calving and lactation period, and certain habitats may provide relief from such irritation (Walsh et al. 1992). Satellite tracking of the Porcupine caribou herd in Alaska allowed these authors to determine habitat preferences during periods of predicted harassment by insects (notably mosquitoes, Culicidae). During such periods, caribou used areas adjacent to the Beaufort Sea coast to gain relief from insect bites. Additionally, survey ﬂights showed that segments of the herd often followed the coastline while moving across the coastal plain of the Arctic National Wildlife Refuge (ANWR) in July. Animal welfare problems were anticipated by these authors if the ANWR were to be opened up to petroleum industry development. They stressed that interactions between caribou foraging opportunities and insect densities at potential relief sites are necessary to determine what might be the consequences to the caribou population if access to relief habitat were to become restricted by development. Reindeer/caribou are least likely to avoid development areas when plagued by insects (Wolfe et al. 2000). The South Georgian reindeer (Fig. 12) are remarkably fortunate in being free from the large number of parasitic ﬂies and other species which torment reindeer in the northern hemisphere (Headland 1984). Canadian caribou are migratory, remaining along the coastal plain of eastern Ungava Bay or, in some cases, along the Labrador coast during winter but moving into the mountains during calving and summer (Schaefer & Luttich 1998). Feeding conditions for Svalbard reindeer (R. t. platyrhynchus) are generally poor, owing to low forage availability. There it has been reported that reindeer have turned to goose faeces as an alternative foodstuff (van der Wal & Loonen 1998). Fresh droppings of barnacle geese (Branta leucopsis) were readily consumed by reindeer and those that contained grass fragments were preferred to those containing moss fragments. Heavy grazing by extremely high densities of reindeer on St Matthew Island (Bering Sea) resulted in degradation of the lichen stands there and (initially) in an increase in vascular plants (Klein 1987). 562

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Figure 12 Reindeer (Rangifer tarandus) cow foraging along the tide-edge vegetation untroubled by ﬂy harassment at Husvik, South Georgia, Southern Ocean (Photo: P. G. Moore).

Primates The Chacma baboons (Papio ursinus) in the Cape Peninsula National Park, South Africa, have been observed scouring kelp beds at low tide for cat shark eggs (Haploblepharus spp.) (C. L. Grifﬁths, pers. comm.). Recent behavioural observations of the Cape Point troop by Ms S. Amien (University of Cape Town) suggest that these baboons only use the shore irregularly. Subadults and females with offspring may opportunistically try to eat a limpet or mussel (S. Amien, pers. comm.). Observations on the behaviour of a troop of yellow baboons (Papio cynocephalus) were made on the Somali coast by Messeri (1978). During the dry season, the baboons lived in the more fruitful mangrove. Sometimes during the morning, at low tide, groups of two or three baboons wandered onto the beach, where they fed on crabs (Ocypode spp.). Remains of O. cordimanus and O. khüli were found on the beach and O. ceratophthalmus on the sublittoral platform afterwards. In some cases crabs were captured with a jump but in most cases the baboons sat down and excavated the holes of the crabs to a depth of 20 cm. It was even possible that they may have used a ﬂotsam cuttlebone as a digging tool (Messeri 1978). An individual tufted capuchin (Cebus apella apella) has been reported tool-using to predate oysters (Crassostrea rhizophorae) in brackish-water mangrove swamps in Maranhao, Brazil (Fernandes 1991). Rhesus macaques (Macaca mulatta) have been observed drinking sea water that ﬂowed into holes they had excavated in sand on Key Lois Island (Florida). Adult females drank and dug most often, perhaps related to social status. Of the total drinking and digging bouts observed, 76.1% were concentrated on one hole which had the lowest salinity of the four holes investigated. 563

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The holes may have been excavated in order to overcome a temporary shortage of freshwater (Lehman et al. 1993). Some macaques, especially the black ones in Sulawesi (Macaca nigra), use the sea shore and have been observed “frolicking” in the waves in the month of August (P. Bright, pers. comm. to D. Morritt). The long-tailed or crab-eating macaque (M. fascicularis) is omnivorous (Quammen 1996). It often feeds on crabs and bivalves in the coastal fringing forests of Southeast Asia (Salter & MacKenzie 1981) and has the habit of uprooting mangrove (Rhizophora) propagules, although few of these are even slightly damaged, much less eaten (Hogarth 1999). Fernandes (1997) reports them eating bivalves (Anadara granosa), a resource hardly exploited by other primates. Attenborough (1984) described long-tailed macaques wading fearlessly on their hind legs and also noted their particular fondness for crabs. They will sit patiently just behind a crab hole until the crab appears and then grab it. Such sit-and-wait behaviour can have unfortunate consequences. Galdikas & Yaeger (1984) describe an immature Macaca fascicularis being taken by a crocodile (Tomistoma schlegeli) from a riverbank in Indonesia. Incidentally, Quammen (1996) noted that while the highly adaptable Macaca fascicularis “is capable of spreading across a tropical continent like a tribe of Visigoths” – it required human intervention for it to reach Mauritius – whereupon it played a role in the extinction of the dodo (Raphus cucullatus). In southern Senegal (West Africa), the vervet monkey (Cercopithecus aethiops) ate both ﬁddler crabs (Uca tangeri) and Rhizophora ﬂowers, fruit and young leaves (Hogarth 1999). According to Galat & Galat-Luong (1976), they spent 52% of their feeding activity hunting ﬁddler crabs and 22% eating Rhizophora (including the pith of young stilt roots). In southeastern Florida, vervet monkeys in mangrove ecosystems ate mostly seeds (23.2%), ﬂowers (21.8%), fruit (20.2%) and invertebrates (17.4%) (Hyler 1995). Some monkey species in mangroves are exclusively herbivorous, including the colobine monkeys, such as langurs or leaf monkeys (Presbytis) and the striking proboscis monkey (Nasalis larvatus). Proboscis monkeys are proﬁcient swimmers (Hazebroek & Abang Kashim bin Abang Morshidi 2001). They must consume large amounts of this relatively indigestible food in order to obtain sufﬁcient energy, ﬁbre, salt and other minerals to satisfy their intake requirements in coastal areas (Bennet & Sebastian 1988). Sonneratia appeared to be the preferred mangrove species, where available, followed by Avicennia (Salter et al. 1985). In Sarawak, proboscis monkeys preferred the riverine forest most of the year, but during the wet season (November to February), when there was a reduction in availability of fruit and young leaves in the riverine forest, they preferred mangrove (Hazebroek & Abang Kashim bin Abang Morshidi 2001). The threatened ebony leaf monkey, Trachypithecus auratus, has been studied on the islands of Java, Bali and Lombok. It occurs in a variety of forest types including mangrove and beach forest (Nijman 2000). The related silvered leaf-eating monkey (T. cristatus) is a folivorous species inhabiting mangrove and coastal forests from Brunei to Vietnam (Fig. 13). It prefers to eat immature leaves since they contain less lignin and tannins (http://www.primate.wisc.edu/pin/factsheets). These monkeys have sacculated stomachs to assist in the microbial breakdown of cellulose (note Bauchop 1978). Many primate species make only intermittent use of mangove forests, as exempliﬁed by bearded sakis (Chiropotes satanas satanas), a typical species of the dense rain forest, which has been seen foraging on insects at the edge of mangroves in northeastern Brazil (Fernandes 1997). 564

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Figure 13 Silvered leaf monkey or langur (Trachypithecus cristatus = Presbytis cristata) which occupies largely the same coastal forest types as proboscis monkeys in Sarawak (Photo: H. Hazebroek).

Nutrient cycles inﬂuenced by activity of coastal mammals Mammals (like humans; Eckrich & Holmquist 2000) will trample organisms underfoot and spread dung and either defaecated or externally adherent seeds as they go (Kerley et al. 1996, Ratnaswamy & Warren 1998, Kiviniemi & Eriksson 1999). While animal dung is a likely cause of hypernutriﬁcation, livestock grazing remains an essential management tool for coastal grazing marshes (Samuels & Mason 1997). Intensive agriculture causes problems 565

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of nutrient outwash and eutrophication, a single pig producing some 2 t of excreta yr−1 (Whittemore 1995), a single cow some 8 t yr−1 (Mason 1976). Hypernutriﬁcation effects resulting from agricultural runoff may already have initiated profound changes in the structure and function of some littoral ecosystems, for example, the Ythan estuary in Aberdeenshire, Scotland (Raffaelli et al. 1989, 1999, Domburg et al. 1998). Similar effects are seen on populated coral cays (Smith & Johnson 1995). The impact of livestock husbandry practices on the nutrient and microbiological quality of fresh (Phillips-Howard 1985) and inshore (Wyer et al. 1999) waters has become a matter of increasing concern in recent years, particularly in European seas where efforts have to be made to treat sewage efﬂuents to meet the EEC Bathing Water Quality Directive Standard (EEC 1976). Very often, inshore coliform counts remain stable in spite of huge investment in sewage treatment and the provision of longshore outfalls. It is presumed that this mismatch is caused by outwash from high intensity livestock subcatchments (Wyer et al. 1999), among a range of other possibilities (Wyer et al. 1997). Jones et al. (1999) found that shedding rates of thermophilic campylobacters (mainly Campylobacter jejeuni) in the faeces of sheep was highest during saltmarsh grazing (higher than upland fell or farm grazing), so that the microbiological consequences of saltmarsh-grazing practices require special attention. The highest rates of campylobacter shedding coincided with lambing, weaning and movement to new pastures. Strange (1992) noted that the dense aggregations of ground-nesting birds (petrels, shearwaters, Magellan penguins), together with seals, supply essential fertilising excrement to coastal tussac (tussock) grass communities on subantarctic islands. Tussac pedestals receive appreciable amounts of volatilised nitrogen from such sources, particularly when excrementrich ground is moistened by salt spray and warmed by the sun. Under such conditions a visible vapour forms that is dispersed over a considerable area (Strange 1992). Domesticated animals, wildlife and seabird colonies contribute signiﬁcantly to ammonia emissions from non-agricultural sources even in the UK (Sutton et al. 2000). By contrast, sand-dune ecosystems are notably nutrient-poor, particularly so far as nitrogen is concerned (Arun et al. 1999). Larter & Nagy (2001) were able to assess winter nutritional deprivation of muskoxen (Ovibos moschatus) on Banks Island, Northwest Territories (Canada) by analysing urea nitrogen : creatine and cortisol : creatine ratios in snow-urine samples.

Ephemeral impacts of coastal mammals The chemical interactions (social signalling) between mammals (cf. Goszczynski 1990, Vila et al. 1994, Leus et al. 1996) in coastal situations are largely unknown (Kruuk 1995), so their ecological signiﬁcance cannot yet be conjectured. Olfaction is unquestionably the dominant sense in wet-nosed mammals (Wynne-Edwards 1967). Urine marking at food and caches has been described for the wolf and the coyote (Harrington 1981, 1982). Clearly, chemical cues would be more ephemeral in tide-washed areas than inland. Inland, footprint scent as detectable by gundogs lasts for varying periods depending on species: said to be 30 min (fox), 5 h (man), 6 h (deer), 2 days (otter), according to its composition (Cadman 1974). The (foot) scent particles of otters, being fatty, last longer (Cadman 1974). The grey seal (Halichoerus grypus) secretes oily substances that leave a pungent odour where they have been resting (Lawrence & Brown 1967). It was contended by Payne-Gallwey (1882) 566

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that most animals dread the smell of seal. Polar bears are very discriminatory of seal odour (see p. 547). Forty-six volatile compounds have been found by Gassett et al. (1996) in secretions from the interdigital gland of male white-tailed deer (Odocoileus virginianus), including alkanes, arenes, aldehydes, ketones, aliphatic acids, esters, pyrroles, furans and sulphur compounds, which acted as generalised trail markers. Scent longevity of urine, faeces and musk territorial marks will also vary widely between species. Kruuk (1992) reported that sprainting (depositing faeces as scent marks at territorial “seats”) was seasonal in otters in Shetland. High sprainting rates coincided with low food availabilities. More than 30% of spraints were deposited in places that ﬂooded within hours and the spraints were functional for only a few hours. Kruuk argued that it was advantageous for otters to signal to other group members when they are exploiting a “patch”, and for other members to avoid resources already partly depleted by a prior arrival. With such a signalling system there is no need for actual aggressive encounters to reinforce the message conveyed by scent marking. Cattle avoid grazing near conspeciﬁc faeces for more than a month, in response to volatile compounds emitted from cow pats (Dohi et al. 1991).

Pollution and coastal mammals At the top of the trophic pyramid in the sea, marine mammals (along with ﬁsh-eating birds) have become a cause for concern relating to pollution bioaccumulation and residue transfer (Norstrom & Muir 1994, Prestrud et al. 1994, Becker 2000, Ross 2000, Weisbrod et al. 2001), ultimately perhaps to humans (Dewailly et al. 1992, Cameron & Weis 1993, Kuhnlein et al. 1995, Chan & Receveur 2000), so interest in these organisms is understandable and not merely esoteric. However, since these species are rare and valued it is perhaps more appropriate to use their prey for monitoring purposes (Khlebovich 1997). Xenobiotics, e.g. endocrine disrupters, contribute to groundwater pollution problems (as do nutrients) caused by irrigation with human sewage and agricultural efﬂuent (Bouwer 2000). Environmental oestrogens may have synergistic effects even at low concentrations (Kortenkamp & Altenburger 1999), and though their effects have been most noticeable in freshwater ﬁshes, marine ﬁshes and mammals are also affected (see above and Gildersleeve et al. 1991, Hylland & Haux 1997, O’Shea in Reynolds & Rommel 1999, Watanabe et al. 2000, Weisbrod et al. 2001). Fish-eating seals, dolphins and whales often contain very high concentrations of lipidsoluble xenobiotics (environmental oestrogens), and in some parts of the world, cetaceans washed ashore have such high concentrations of pollutants in their bodies that carcasses have to be disposed of as toxic waste. At present, an accumulated “weight of evidence” suggests that body concentrations of lipophilic contaminants have adversely affected aspects of reproduction, immune and endocrine function in marine mammals inhabiting a number of industrialised coastal regions (Crain et al. 2000, Minh et al. 2000, Ross 2000). Prestrud et al. (1994) thought the concentrations of heavy metals in Arctic foxes on Svalbard were most likely of natural origin. Boon et al. (1997) considered datasets on chlorobiphenyl (CB) residues in ﬁsh-eating mammals from ﬁve laboratories. Clear differences in polychlorinated biphenyl (PCB) patterns were observed between species. The ability to metabolize CB congeners with vicinal H-atoms only in the ortho- and meta-positions (Fig. 14), and with one ortho-chlorine substituent generally increased in the order otter < cetaceans (harbour porpoise, common dolphin) < phocid seals (harbour and 567

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Figure 14 Conﬁguration of a generic polychlorinated biphenol (PCB) molecule, where vicinal hydrogen atoms at X can be variously substituted by chlorine atoms at different meta, ortho, or para positions on each ring. Some 209 different isomers and congeners are possible, though a smaller number are usually reported as biological contaminants.

grey seal). However, the metabolism of congeners with vicinal H-atoms in the meta- and para-positions and with two ortho-chlorines increased in the order cetaceans < seals < otter. Both categories of congeners are probably metabolised by different families of cytochrome P450 (1A and 2B), of which levels apparently differed between the cetaceans, the pinnipeds, and the otter. Tissue concentrations of DDTs and PCBs peaked in the 1970s and were associated with an increased incidence of premature births in California sea lions (Zalophus californianus) (De Long et al. 1973). Alarmingly high PCB concentrations have also been found in polar bears at Svalbard (Skaare et al. 2000). Chemical contaminants of stranded grey whales (Eschrichtius robustus) along the west coast of North America were consistent with a geological source of elements from the ingestion of bottom sediment while feeding, and concentrations of anthropogenic chemicals (chlorinated hydrocarbons) showed little relation to concentrations at the stranding sites (Varanasi et al. 1994). The problems relating to chronic impacts of oil pollution on otters have been alluded to above (p. 539, Duffy et al. 1994, Bowyer et al. 1995). McKay et al. (1993) showed that concentrations of a range of radionuclides, although small, were clearly in excess of background levels in soil cores taken from tide-washed pastures in southwestern Scotland, conﬁrming that there was a signiﬁcant contribution from the Sellaﬁeld nuclear plant’s marine discharges to the Irish Sea in Cumbria. However, the maximum annual dose calculated to arise to humans (assessed from a habit survey of users) was 60 µSv, which is less than 6% of the ICRP principal dose limit of 1 mSv for members of the public (1 Sv = 102 rem). Sanchez et al. (1998) measured the activity concentrations of 137 Cs, 238Pu, 239Pu, 240Pu and 241Am in root mat and vegetation samples collected from tidewashed pastures in 17 estuaries spanning the eastern seaboard of the Irish Sea, extending from the Solway in northwest England to St David’s Head in south Wales. Dose assessment calculations suggested that external exposure (i.e. from the background: including cosmic radiation, natural radioactivity of sea water and sediments) would be a maximum of 530 µSv at the most contaminated spot in the Esk estuary. They pointed out that relatively lower 568

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doses would arise from the ingestion of animal products (along the soil-vegetation-grazing animal pathway) due to the low availability of sediment-associated radionuclides for gut transfer (see also Howard et al. 1996). Vegetation contamination with radionuclides (137Cs, 239/240 Pu & 241Am) in the Mersey estuary was almost entirely due to external contamination by sediment, with marked temporal variation being consistent with contamination by deposition of suspended sediments during tidal inundations (Jones et al. 1994, note also Horrill 1984). Transfer coefﬁcients (i.e. the fraction of daily intake in food passing into body tissues), of 137Cs were signiﬁcantly higher in saltmarsh sheep near Sellaﬁeld than in inland pasture sheep (Howard 1987). Experimental work (Howard et al. 1989) showed that transfer of radiocaesium to ewe tissues and milk was greater from herbage contaminated by the Chernobyl fallout than from saltmarsh vegetation contaminated by marine discharges from the Sellaﬁeld reprocessing plant. (For more details on uptake of radionuclides by sheep grazing on estuarine saltmarsh, see Howard 1985a,b.) Availabilities of radionuclides to mammals outside the human food chain remain largely unstudied. That said, the radioactivity in otter (Lutra lutra) scats was determined in 1986 and 1987 after the Chernobyl nuclear accident (25–26 April 1986) by Mason & Macdonald (1988). They collected material from various regions of Great Britain, including from the seashore adjacent to the Dounreay nuclear power station in Caithness (North Scotland), and compared those with control sites. Samples collected from central Wales, Galloway and northern Scotland all contained signiﬁcantly higher amounts of radioactivity than a 1985 sample from central Wales, with Galloway having signiﬁcantly more radioactivity than other regions.

Climate change and coastal mammals The correlation between areas of open water in ice-covered seas and increased biological productivity has been noted for some time. Projections of climate models suggest that, if global warming occurs, then the extent of ice cover in Hudson Bay, Canada may be among the ﬁrst things affected. Long-term studies of polar bears and ringed seals in the eastern Beaufort Sea and Hudson Bay suggest that these two species may be suitable indicators of climatic or oceanographic changes in the marine ecosystem (Stirling 1997). In September 1971, Soviet aviators claimed that walrus movements provided valuable information on weather changes and ice condition. They claimed that if herds of walrus appear at unlikely places, such as near large packs of ice, then within a few days the sea would become icefree. Conversely, their disappearance from an apparently favourable area would foretell a fall in temperature (Mallinson 1978). Carbon-14 dating of whalebone material incorporated into raised beach deposits on coastal forelands have facilitated accurate dating of the retreat of the Barents Sea ice sheet from western Franz Josef Land, Russia (Forman et al. 1996). Of all the coastal habitats in Scotland, machair is possibly the one most threatened by climate change. Sea level rise and increased incidence of severe winter storms both pose a threat. Machair slopes downward inland, and any breach of the dunes could result in machair being replaced by salt marsh or sand ﬂats. The highest conservation value of machair ecosystems comes from rearing of cattle and the associated fodder crops by traditional methods, and this will become difﬁcult, perhaps impossible, if more machair gets ﬂooded by salt water (Angus 2001). That this habitat, one of the most striking positive examples demonstrable of 569

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sensitive human land management that is beneﬁcial both to crofting communities and to a staggering array of wildlife, should be threatened in this way is potentially tragic. Environmental catastrophes in the form of ﬂash ﬂoods may result in mass mortalities, for example, of ﬁshes, delivered to estuarine deltas (Whitﬁeld & Paterson 1995). Severe storms (hurricanes, tsunamis) impact on coastal regions, perhaps sweeping away whole communities of vertebrates (Gardner et al. 1992), even of humans (Tsuji et al. 1995). Some of the stranded carcasses so produced will become available to scavenging mammals. In Northern Australia, low-lying Holocene coastal plains supporting freshwater wetlands are often close to, or even below, the elevation reached by the highest tides. The vulnerability of such freshwater systems to saltwater intrusion will increase should sea level rise as a consequence of global warming. In areas with a large tidal range, one of the main processes inducing a reversion to saltwater inﬂuence is likely to be the rapid extension of tidal-creek systems. Such systems have extended more than 30 km inland in the Mary River region over the last 50 yr, invading freshwater wetlands and destroying associated vegetation over an area of at least 17 000 ha. Large tidal range, very small elevational differences over the plains, the presence of incompletely ﬁlled palaeochannels, and trampling by an uncontrolled feral Asian water buffalo (Bubalus bubalis) population have been major factors contributing to the rapid rate of saline expansion (Knighton et al. 1991, Mulrennan & Woodroffe 1998). By contrast, in another low-lying area, the Netherlands, sedimentation on mainland saltmarshes is expected to compensate for the anticipated sea-level rise, though this may not be the case for island salt marshes if the relative sea-level rise is more than 0.5–1.0 cm yr−1 (Bakker et al. 1993). Saltwater intrusion frequently destroys coastal rice crops in central Vietnam, due to high waves generated by typhoons. Such events have consequences for coastal livestock husbandry practices.

Conservation and coastal mammals From a conservation viewpoint the continuance of plant and animal communities in coastal environments, all of which have strong complex gradients, depends on the existence of speciﬁc links (neighbourhood effects) and high connectivity between habitat islands. Problems arise when communities become disrupted, habitats become fragmented into small patches, or eventually disappear (Preen & Marsh 1995, Forys & Humphrey 1996, Mallick et al. 1997, Telleria & Virgos 1997, Povilitis 1998, Chiarello 1999, Acosta et al. 2000, Waiyaki & Bennun 2000, Kathiseran & Bingham 2001, Oli et al. 2001). Offshore islands may, however, be useful refugia for endangered species, as witness the Perdido Key beach mouse (Peromyscus polionotus trissyllepsis) which was re-established on Gulf Islands National Seashore (Florida) after declining to some 30 animals at Perdido Key (Alabama) (Holler et al. 1989, Oli et al. 2001). This mouse was once present in more or less continuous populations in coastal dune habitat in Florida and Alabama but, by 1985, it was the most endangered of the three subspecies of beach mice listed as endangered by the US Fish and Wildlife Service (Holler et al. 1989). The key conservation measure was to avoid loss of scrub dune habitat (Oli et al. 2001). The linkage between coastal dune proximity to adjacent forest was highlighted by Ramsey & Engeman (1994) as a determinant of macropod grazing pressure in an offshore island off Queensland, Australia. Contiguous habitats and connectivity, as well as habitat structure and integrity, are thus key issues in 570

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determining an area’s suitability for coastal (as for other) mammals (Fitzgibbon 1994). Walsh & Harris (1996) have stressed the importance of water margins and linear corridor habitats for bat populations. Forys & Humphrey (1996) found that the endangered marsh rabbit (Sylvilagus palustrishefneri) used mangrove tracts as dispersal corridors between marsh habitats. Mammalian redistribution of seeds can be an important plant dispersal mechanism between fragmented habitats (note Kiviniemi & Eriksson 1999 in a rather different context). Kerley et al. (1996) showed that bushpig, Potamochoerus porcus, and vervet monkeys, Cercopithecus aethiops, transported signiﬁcant amounts of nutrients (and dispersed seeds of 29 plant species) in coastal duneﬁelds at Alexandria (Eastern Cape, South Africa). Although 80% of the duneﬁeld was bare sand, diverse biota were supported in plentiful bushpockets, typically of c. 1 ha in extent. The bushpocket communities were dependent upon the activities of mammals and birds) for their maintenance. Small mammal diversity was independent of bushpocket size, but was a function of vegetation structure. Seed consumption in the bushpockets (mostly by endemic dune gerbils ?Tatera afra) was greater than that in vegetation inland of the dunes. These authors stressed that duneﬁeld management should include management of animal and plant communities adjacent to the duneﬁeld (note also Moller et al. 1998). Some sand-dune plantations, however, like pine (Sturgess & Atkinson 1993) have little botanical conservation value and these authors favoured clear felling and needle litter removal to facilitate habitat restoration. Needle litter inhibits germination from the original sand-dune seed bank but the consequences of its removal on small seed-eating mammals foraging for food was not addressed. It was suggested by Chase et al. (2000) that, in order to conserve small mammal and bird biodiversity in coastal sage scrub in California, effort should not focus exclusively on rare species or on locations with the highest species richness. Instead it should encompass a diverse suite of species that are representative of the range of variation in communities found in coastal sage scrub habitats. Attention was focused by Kruuk (1995) on the association between the presence of otters and the absence of coastal agriculture to explain their prevalence on Shetland and scarcity on Orkney. Where moorlands or woods abutted the shoreline, otters were prevalent. Just how much human activities actually deter otters in the northern isles, however, is contentious (S. Angus, pers. comm.). Habitat conservation is a key issue worldwide. Most North Sea coastal grazing marshes originated through the enclosure of estuarine salt marsh for agricultural purposes by the construction of an embankment, and now support important plant and bird populations. Extensive areas of ﬂat grazing marsh are found in southeastern England along the coasts of Essex and north Kent. These are now under great pressure for “improvement” by deep drainage for more intensive agriculture or as a prelude to development, inter alia threatening characteristic ditch biota. Over 60% of this habitat type has been lost on the Thames estuary already, and in Essex this loss amounts to 82% over the last 50 yr (J.N.C.C. 1993). Breeding densities of redshank (Tringa totanus) were highest on lightly grazed salt marshes and lowest on heavily grazed marshes in Great Britain. An increase in grazing intensity between 1985 and 1996 was the most likely explanation for the decline in redshank over the same period (Norris et al. 1998). The Petit Loango Game Reserve, in Gabon, where dense tropical forests come all the way down to the shore, is one of the few places where forest elephants (Loxodonta africana cyclotis), buffalo (Syncerus caffer) and gorillas (Gorilla gorilla gorilla) can be seen on the beach. Imminent and current threats (logging, oil exploration, poaching) have prompted a 571

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World Wildlife Fund project in this very special location (Ramsar site, No. 352). Forty years (or more) ago, the Arabuko Sakoke forest in Kenya, the largest stretch of coastal forest left on the east coast of Africa, came right down to the beach. Even after clearance of the coastal strip for 2–3 km inland, lions (Panthera leo) and leopards (Panthera pardus), even elephants, are still occasionally to be seen on the adjoining beaches (H. Curtis, pers. comm. to S. Jones). Bark stripping and other elephant damage are considered to be major threats to East African coastal forests (Waiyaki & Bennun 2000). A survey of the terrestrial mammal fauna of Muni-Pomadze Ramsar site along the Ghanaian coastline revealed 22 species, with smallmammal communities being most diverse in the grassland – thicket habitats surrounding the lagoons (Ryan & Attuquayeﬁo 2000). Insufﬁcient literature exists on lowland rainforests in the New World, for example, on mammalian diversity in coastal Venezuela (Voss & Emmons 1996), to draw intercontinental comparisons. Protection may bring negative consequences. For instance, in the South American fur seal (Arctocephalus australis) population at Punta San Juan, Peru, females pupping around the peak of births had a greater probability of losing their pups, and/or tended to lose them at an earlier age than females breeding early or late in the season. High breeding densities and consequent high pup mortalities may have resulted from intense poaching outside protected areas and are of recent origin (Majluf 1992). Diseases also pose serious threats to rare species, and mammalian carnivores on islands may be especially susceptible. Seventy-eight per cent of island foxes, a dwarf race of grey fox (Urocyon littoralis), tested positive for heartworm (Diroﬁlaria immitis) antigen on Santa Cruz island, compared with 5% incidence in grey foxes on mainland California (Crooks et al. 2001b). Insular endemic species may therefore be vulnerable to exotic parasites and their vectors (Crooks et al. 2001a).

Possible areas for future research A great drawback to our precise understanding of the role played by mammals in nearshore habitats remains the dearth of night-time observations. The nocturnal activity of small terrestrial mammals will account for much understatement in the scientiﬁc literature relating to shoreline ecology. Marine mammals, of course, because of their large body size, mobility and abundance, play a major role in the structure and function of some aquatic communities (Bowen 1997). The spatial scale of any impacts of larger mammals on land (Hammill & Smith 1991, Stempniewicz 1993, Ferguson et al. 2000, Samelius & Alisauskas 2000) is likely to be different from that of other predators (invertebrates; Paine 1969, wading birds; Puttick 1984, Cummings et al. 1997) foraging along shorelines, in relation to scale differences in both prey aggregations and the patch dynamics of local habitats (cf. Gaona et al. 1998). Although radio-tracking has been applied to sand-dune mice (Corp et al. 1997, note also Moro & Morris 2000), coastal foxes (Meek & Saunders 2000) and polar bears (Amstrup 1993), this technology has been little exploited for studying the movements of mammals in coastal ecosystems, even though it is now widely used on many other organisms (for examples, see Rodda 1983, Weavers 1992, Gardner & Serena 1995, Lode 1995, Dunham 1998). Remote data logging of activity patterns can overcome many of the problems associated with the secretive, and frequently nocturnal, habits of mammals, as can multiple automatic camera systems (Sweitzer et al. 2000). In some situations, however, old-fashioned tracking can have advantages over radio tracking, especially in offering evidence of an animal’s 572

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behaviour to the experienced eye (Matyushkin 2000). Thermal imaging techniques might also hold promise (Gill et al. 1997) and aerial surveying can be used to good effect on large conspicuous species (Bashore et al. 1990), although this needs to be done sensitively to avoid any unfortunate disturbance effects from ﬁxed-wing aeroplanes or helicopters (Ovsyanikov et al. 1994). Aerial dye-spray marking of caribou was successfully applied by Miller et al. (1977). Remote sensing of ringed seal ice holes has been described by Digby (1984). Driessen et al. (1996) investigated the habitat requirements of the eastern barred bandicoot (Parameles gunnii) in Tasmania, and established that the species was uncommon on the east coast, by analysing road kills. The activity pattern of a coast mole (Scapanus orarius) was monitored by Schaefer (1982) by tagging a hindlimb with a copper band labelled with 192Ir. Candles were used as gnaw sticks to discover the distribution of rats on Frégate Island, Seychelles by Thorsen et al. (2000). Stable isotope analysis allows differentiation of dietary components derived from marine and terrestrial sources (Angerbjorn et al. 1994). The opportunities represented by enclosure/exclosure experimentation, although well established for large grazers on grassland and freshwater streams (Rinne 1988, Painter et al. 1993, Sidle & Sharma 1996, Beever & Brussard 2000), seem hardly (but note Furbish & Albano 1994) to have been exploited for mammalian research in coastal ecosystems. Too little is known either about the chemical ecology of mammalian signalling (see p. 566) or the chemical feedback mechanisms between grazers and that which is grazed. The chemical basis for intraspeciﬁc variation in susceptibility to Eucalyptus herbivory by marsupial folivores, like ringtail possums (Pserrdocheirus peregrinus), was investigated by Lawler et al. (2000). Of a variety of formylated phloroglucinol compounds investigated by Eschler et al. (2000), derived from 41 species of Eucalyptus growing on the east coast of Australia, the most frequently identiﬁed group of compounds were the sideroxylonals. Signiﬁcant patchiness in nutritional quality of foliage, resulting from variation in foliar concentrations of a single compound, existed at a scale relevant to the feeding decisions of individual animals (Lawler et al. 2000). Wounding of both terrestrial and marine plants is known to induce chemical responses; the induced compounds (e.g. phlorotannins in kelps) most likely defending against herbivory (Hammerstrom et al. 1998) or acting in wound-healing processes. Bolser & Hay (1996) reported latitudinal trends in palatability of seaweeds to sea urchins, with tropical macroalgae generally having stronger antigrazer defences than temperate seaweeds, but comparable data relating to grazing mammals are lacking. Further detailed work is required upon the biological consequences (e.g. recovery of diversity and productivity) of programmes seeking to eradicate introduced mammals from offshore islands, including estimations of the efﬁcacy of different extirpation methods. Empirical studies (and not only for mammals) on demographics, dispersal patterns and gene ﬂow in metapopulations of species in increasingly fragmented coastal habitats are called for (Gaona et al. 1998), using DNA ﬁngerprinting techniques (Holler et al. 1989), to complement modelling work. There is scope also for more research on direct interactions between coastal livestock and wildlife (Beever & Brussard 2000). The impact of livestock production, via egress of nutrients and microbes, on estuaries or adjacent open coasts will also require increasing attention as intensive farming practices are scrutinised more closely. Any moves towards more holistic and sustainable Integrated Coastal Zone Management (ICZM) practices are to be welcomed. Clearly, these need to include mammalian issues (like grazing management regime, McCracken & Bignal 1998, Norris et al. 1998) and far-ﬁeld effects (as described above, p. 565; see also Banens & Davis 1997, Shamsudin 1999, 573

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Yanez-Arancibia et al. 1999). ICZM practices should be politically sensitive enough to build-in traditional ecological knowledge of indigenous peoples (Baldwin 1988), and help to empower and educate local communities (Baldwin 1988, Crance & Draper 1996, Jacoby et al. 1997). They should focus on the growing problems of habitat loss, coastal redevelopment (see p. 492) and ecotourism, all of which human activities threaten coastal mammals (Agardy 1993, Wall 1997). Thus the shrinking population of manatees caused by habitat loss, pollution, disturbance and rampant urbanisation prompted the State of Florida to pass the Manatee Sanctuary Act in 1978 (Amsler 1995). The most serious threat to manatees, especially in Florida, is speedboat propellers (Branson & Branson 2000), especially when recreational boating co-exists with manatee use of thermal refuges created by warm-water springs during winter cold periods ( Buckingham et al. 1999). The expansion of ecotourism has also threatened manatees in coastal waterways in Costa Rica by increasing boat trafﬁc; as have other activities including logging and cattle ranching (Smethurst & Nietschmann 1999). Similar manatee and watercraft problems are reported from Puerto Rico by Mignucci-Giannoni et al. (2000a). Regeneration of cutover coastal forests may be adversely affected by destruction of the seed supply by small mammals (inter alia), for example, the deer mouse (Peromyscus maniculatus) in British Columbia (Sullivan 1979). More multidisciplinary research is needed to understand coastal ecosystems, and it is to be hoped that mammalian aspects of these issues will no longer be overlooked through lack of collated background material for reference.

Acknowledgements I wish to thank the following of my friends, old and new, who have racked their brains and ﬁling systems for information relevant to the topic in hand, and for generously sharing their gleanings with me: Shireen Amien, Stewart Angus, Jim Atkinson, Clive Craik, Steve Gorzula, Rosemary Green, Charles Grifﬁths, Phil Hammond, Fiona Hannah, Peter Hogarth, David Houston, Samantha Jones, David Morritt, Rupert Ormond, Ian Patterson, Alastair Richardson, Colin Shedden and Sean Twiss. Xavier Lambin kindly agreed to allow me to quote from unpublished work. Several of them have read through various interim drafts of the MS and given freely of their comments and criticisms, for which I am truly grateful. Stewart Angus, Rod Braby, Fiona Guinness, Hans Hazebroek, Jan Nel and Richard Welsby kindly supplied photographs, which are reproduced with their permission. The IT skills of Ian Ramsden and Steve Parker helped process illustrations. To the editors I express my thanks both for their constructive criticisms and for their usual care and attention to detail. Any errors which remain are, of course, purely my own responsibility.

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AUTHOR I ND E X

AUTHOR INDEX

References to complete articles are given in bold type; references to bibliographical lists are given in italics; references to pages are given in normal type. Aarnio, K. See Bonsdorff, E., 479 Aastrup, P., 492; 574 Abal, E.G., 262, 263; 293 Abang Kashim bin Abang Morshidi. See Hazebroek, H., 519, 542, 560, 564; 589 Abbott, I., 495; 574; See Burbidge, A.A., 579 Abdel-Fattah, H.M. See Moubahser, A.H., 597 Abele, L.G., 323; 335 Aberle, N. See Witte, U., 232 Ablain, M. See Dorandeu, J., 29 Abouheif, M.A., 500; 575 Abramsky, Z. See Rosenzweig, M.L., 323; 339 Abuabara, J.Y. See Otte, M.J., 598 Ackleson, S.G. See Yoder, J.A., 232 Ackley, S.F., 144, 147, 152; 162; See Dieckmann, G.S., 162; See Eicken, H., 163; See Fritsen, C.H., 163; See Horner, R., 164; See Lange, M.A., 165; See Legendre, L., 165 Acosta, A., 570; 575 Acuna, M.T. See Campos, E., 579 Adam, P., 521; 575 Adams, J.B., 252, 256, 258, 263; 293; See Snow, G.C., 306; See Wortmann, J., 309 Adams, T.J.H. See Dalzell, P., 413 Adcroft, A., 59, 62, 84; 111 Addink, S.C. See Jauniaux, T., 590 Ådlandsvik, B. See Engedahl, H., 120 Adolfsson-Erici, M. See Smith, S., 488 Aelbrecht, D., 52; 111 Afzelius, L., 466; 478 Agardy, M.T., 574; 575 Agatsuma, Y., 354, 355, 357, 396, 405, 409; 410; See Andrew N.L., 343–425; See Watts, S.A., 424; See Yano, K., 425 Agawin, N.S.R. See Duarte, C.M., 296; See Terrados, J., 307; See Vermaat, J.E., 308 Agnon, Y., 51, 53, 111 Agrenius, S. See Rosenberg, R., 487 Agrimi, U. See Diguardo, G., 582 Aguilar, A. See Forcada, J., 585 Aguilar, D. See Cota, A., 413; See Palliero, J.S., 420

Ahmed, R. See Clarke, A.J., 21; 29 Ainley, D.G. See Ribic, C.A., 601 Aioi, K. See Nakaoka, M., 555; 597 Airoldi, L., 265, 268; 293, 294 Akeda, S. See Yano, K., 425 Aken, K.M. See Salter, R.E., 601 Aksnes, D.L., 107; 111 Alayse-Danet, A.-M. See Sibuet, M., 230 Albano, M. See Furbish, C.E., 503, 566, 573; 586 Alberico, J.A.R., 545; 575 Albertelli, G. See Pusceddu, A., 167 Albiach, J.C.C., 49, 100; 111; See Monbaliu, J., 130; See Ozer, J., 132 Albin, D. See Karpov, K.A., 416 Albon, S.D. See Clutton-Brock, T.H., 560; 580; See Pemberton, J.M., 599 Aldaz, I. See Mauchamp, A., 595 Aldridge, J.N., 43, 91, 93; 111; See Davies, A.M., 69, 85; 117; See Sajjadi, S.G., 75, 76; 135; See Young, E.F., 141 Aleem, A.A., 235, 244; 294; 492; 575 Alenius, P., 439; 478 Alexander, S. See Roughgarden, J., 421 Alheit, R. See Schnack-Schiel, S.B., 168 Alibert, C., 253; 294; See McCulloch, M., 302 Alimov, A.F., 441, 442; 478; See Telesh, I.V., 488 Alino, P.M. See Wesseling, I., 308 Alisauskas, R.T. See Bantle, J.L., 531, 532; 576; See Samelius, G., 532, 572; 601 Allain, J.Y., 390; 410 Allan, R., 240; 294 Allchin, C.R. See Boon, J.P., 578; See Kuiken, T., 593 Alldredge, A.L., 192, 193, 195, 201, 206, 208; 222; See Macintyre, S., 227 Allen, J.A. See Rex, M.A., 339 Allen, J.I., 44, 95, 108; 111 Allen, J.R.L., 496; 575 Allen, J.S. See Newberger, P.A., 131 Aller, J.Y., 179, 190, 194, 209, 219; 222

609

AUTHOR I ND E X

Aller, R.C. See Aller, J.Y., 179, 190, 194, 209, 219; 222 Aller, R.C. See Sun, M.-Y., 231 Allison, M.A., 246; 294 Alongi, D.M. See Snelgrove, P.V.R., 341 Alonso, J.J. See Bruno, M., 115 Al-Owaimer, A.N., 500; 575 Alpers, W. See Brandt, P., 114 Al-Saiady, M. See Abouheif, M.A., 575 Altabet, M.A. See Smith, C.R., 230, 340 Altenbach, A.V. See Jumars, P.A., 226; See Linke, P., 227 Altenburger, R. See Kortenkamp, A., 567; 592 Alva, V. See Sala, E., 421 Alvarado, J. See Castilla, J.C., 412 Álvarez, A., 107; 111; See Pinot, J.-M., 133 Alvarez, L.G. See Lavin, M.F., 301 Alvarez, O., 94; 112 Alvarez-Cardenas, S., 494; 575 Alvarez Fanjul, E., 100, 101; 112 Alvarez-Salgado, X.A. See Rosón, G., 135 Ambrose, D.A. See Yoklavich, M.M., 309 Ambrose, W.G., 188, 206; 222 Amin, M., 45, 68, 69, 92; 112; See Mason, D.C., 129 Amon R.M.W., 157; 162 Amos, C.L., 208; 222 Amsler, K., 574; 575 Amstrup, S.C., 572; 575 Anastasiou, K. See Oliveira, F.S.B.F., 43, 102; 132 Andersen, F.Ø., 476; 478 Andersen, O.B., 23; 28; See Shum, C.K., 33 Andersin, A.-B., 434, 439, 440, 441, 446, 447, 448, 449, 472, 473, 474, 475; 478; See Elmgren, R., 481; See Laine, A.O., 441; 483 Anderson, J.D., 53; 112 Anderson, M.J., 292; 294 Anderson, O. See McShane, P.E., 419 Anderson, P.K., 554, 555, 556; 575 Anderson, R. See Malone, T.C., 228 Anderson, S.S., 510, 549; 575; See Boness, D.J., 578; See Summers, C.F., 604 Andersson, E. See Jansson, R., 299 Andersson, J. See Juhlin, B., 483; See Lindqvist, K., 484; See Smith, S., 488 Andersson, L., 446, 448, 450, 460, 463; 478; See Larsson, U., 428, 444; 484; See Smith, S., 488 Andersson, M. See Jerling, L., 500, 502; 591 Andreoli, C., 144; 162

Andrew, N.L., 236, 268, 279; 294; 343–425; 384, 390, 394, 396, 397, 401; 410; See Byrne, M., 412 Andrews, J.D. See Donaldson, G.M., 583 Andrieux, F. See Guillaud, J.-F., 122 Androsov, A.A., 48, 56, 62, 86, 87; 112 Andrushaitis, A., 442, 443; 478 Angeloni, L. See Crooks, K.R., 581 Angerbjorn, A., 531, 532, 573; 575; See Elmhagen, B., 584 Angot, P. See Verdier-Bonnet, C., 139 Angus, S., 503, 506, 507, 510, 511, 516, 517, 519, 520, 521, 526, 543, 545, 548, 562, 569; 575 Annan, J.D., 104; 112; See Chen, F., 77, 94, 98; 116 Anonymous, 458; 478; 535, 553, 555; 575 Anthony, R.M., 531; 575 Antia, A.N. See Lampitt, R.S., 200, 202, 205, 206; 226 Antolic, B. See Le Direac’h, J.P., 418 Antrim, B.S. See Yoklavich, M.M., 309 Aparicio, F. See Gazo, M., 587 Appleby, C.J. See Archer, S.D., 162 Arakawa, A., 59; 112; See Mesinger, F., 59; 130 Arango, H. See Chassignet, E.P., 116 Archer, S.D., 154; 162 Arcilla, A.S. See Espino, M., 120 Ardhuin, F., 49; 112 Ardisson, P.-L., 246, 277; 294 Arief, D., 21; 28 Armor, C. See Jassby, A.D., 299 Arnbom, T.A. See Fedak, M.A., 585 Arndt, C. See Hahlbeck, E., 482 Arnold, R.J., 56; 112 Arnould, C., 537; 575 Arnoux, A. See Stora, G., 276; 306 Arnoux-Chiavassa, S., 43, 97, 99; 112 Arntz, W.E., 459; 478 Aronson, R.B., 401; 410 Arrigo, K. See Dieckmann, G.S., 162 Arrigo, K.R., 148, 152, 153, 154, 157; 162; See DiTullio, G.R., 224 Artale, V. See Roussenov, V., 135 Arthington, A.H., 236; 294 Arthur, C.P. See Rogers, P.M., 601 Arun, A.B., 566; 576 Ascaray, C.M., 505; 576 Ashworth, C. See McMinn, A., 148, 149, 161; 166 Ashworth, M. See Allen, J.I., 111

610

AUTHOR I ND E X

Asper, V.L. See Diercks, A., 206; 224 Atkinson, D. See Sturgess, P., 571; 604 Atkinson, I.A.E. See Moors, P.J., 493, 536; 596 Atkinson, R., 519, 520, 527, 536; 576 Attenborough, D., 528, 564; 576 Attrill, M.J., 492; 576 Attuquayeﬁo, D. See Ryan, J.M., 572; 601 Au, A.Y. See O’Connor, W.P., 32 Aubrey, D. See Oguz, T., 131 Aubrey, D.G. See Emery, K.O., 25; 29 Auclair, F., 63, 69; 112 Auffret, G., 176; 222 Ault, J.S. See Serafy, J.E., 305 Aure, J., 463, 466; 478 Aurin, T. See Wesseling, I., 308 Austen, M.A. See Smith, C.R., 340 Austen, M.C., 465; 478; See Snelgrove, P.V.R., 340 Avenant, N.L., 535; 576 Avery, D.M. See Avery, G., 576 Avery, G., 533; 576 Avesaath, P.H. See van Katwijk, M.M., 308 Avila Serrano, G.E. See Kowalewski, M., 300 Avlesen, H., 38; 112 Axell, L.B. See Omstedt, A., 59, 89; 132 Axelson, F. See Himmelman, J.H., 415 Axis-Arroyo, J., 554; 576 Ayotte, P. See Dewailly, E., 582 Azam, F. See Bidle, K.D., 156; 162 Aznar, F.J., 528; 576 Babaric, V., 107; 112 Babbs, S. See Norris, K., 597 Babcock, R.C., 399; 410 Babenerd, B., 457, 458; 478 Babione, M. See Valiela, I., 307 Bach, H.K., 46, 99; 112 Bach, S. See Duarte, C.M., 296; See Terrados, J., 307 Bach, S.S., 257, 259, 262; 294 Bachelet, G., 319, 320; 335 Bäck, S. See Kauppila, P., 433, 438, 439, 440; 483 Backhaus, J.O., 45, 78; 112; See Brandt, P., 114; See Fohrmann, H., 121; See Jungclaus, J.H., 125; See Kämpf, J., 55, 82, 90, 93; 126; See Schrum, C., 81, 82; 136; See Simeonov, J., 136; See Verduin, J.J., 99; 139 Baco, A.R. See Smith, C.R., 340 Baden, S.P., 462, 471, 476; 478; See Eriksson, S.P., 476; 481

Badgero, G.A. See Stenson, G.B., 603 Bagge, O., 461, 477; 479 Bagge, P., 438; 479 Bagley, N. See McClatchie, S., 338 Bahamón, N., 43; 113 Bahlo, R. See Leipe, T., 227 Baier, C.T. See Stoecker, D.K., 168 Bailey, E.P. See Byrd, G.V., 579 Bailey, R.T., 62; 113 Baird, A. See Anderson, S.S., 575 Baird, R.W. See Jefferson, T.A., 591 Bak, R.P.M. See Duineveld, G.C.A., 224 Baker, A.G. See Gassett, J.W., 586 Baker, D.J. See Wearn, R.B., 19; 34 Baker, E.K. See Harris, P.T., 298 Baker, J.L. See Jones, G.K., 299 Baker, J.P. See Driscoll, C.T., 296 Baker, J.R., 551, 553; 576; See Anderson, S.S., 575; See Kuiken, T., 593 Baker, R. See Baker, J.R., 551; 575 Baker, T.F. See Spencer, R., 34 Baker, T.F. See Tsimplis, M.N., 22; 34 Bakker, J.P., 501, 502, 570; 576; See Dormann, C.F., 521; 583 Balbuena, J.A. See Aznar, F.J., 576 Balch, T., 398, 405; 410; See Scheibling, R.E., 422 Balch, W.M. See Yoder, J.A., 232 Baldauf, J.G. See Kemp, A.E.S., 215, 219; 226 Baldwin, C., 555, 574; 576 Baldwin, R. See Beaulieu, S., 210; 222 Baldwin, R.J., 181, 187, 204; 222; See Drazen, J.C., 224; See Smith Jr, K.L., 213; 230, 231 Bale, A.J. See Harris, J.R.W., 123; See Jago, C.F., 226 Baliño, B.M. See Aksnes, D.L., 111 Ballard, G. See Prandle, D., 134 Ballesteros, E., 384; 410; See Andrew, N.L., 343–425 Ballesteros, M. See Palacín, C., 420 Balls, P. See Raffaelli, D.G., 600 Bally, R. See Tarr, J.G., 604 Balmino, G., 25; 28 Bambang, Y., 285; 294 Banens, B. See Wasson, B., 308 Banens, R.J., 573; 576 Bangi, H.G. See Juinio-Meñez, M.A., 416 Bangjord, G., See DeRocher, A.E., 582 Banke, E.G. See Smith, S.D., 79; 137 Banse, K., 471; 479 Bantle, J.L., 531, 532; 576

611

AUTHOR I ND E X

Baptista, A.M. See Fortunato, A.B., 121; See Westerink, J.J., 140 Baraille, R. See Hoang, S., 123 Barber, R.T. See Falkowski, P.G., 336; See Yoder, J.A., 232 Bareiss, J. See Haas, C., 164 Baretta, J.W., 95; 113; See Baretta-Bekker, J.G., 113; See Vested, H.J., 139; See Zavatorelli, M., 141 Baretta-Bekker, J.G., 95; 113; See Zavatorelli, M., 141 Bargelloni, L. See Andreoli, C., 162 Barilotti, D.C., 402; 410 Barker, J. See Lunney, D., 594 Barker, M.F., 384; 410 Barker, P., 156; 162 Barlocher, F., 503; 576 Barnard, G. See Chapuis, J.L., 580 Barnes, B. See Holloway, P.E., 123, 124 Barnes, C.R. See Mohan, R.K., 130 Barnes, D.K.A., 391, 406; 411; See Andrew N.L., 343– 425 Barnett, B.D., 492, 537; 576 Barnett, E.J. See Bourman, R.P., 242, 281; 294 Barnett, P.R.O., 190; 222 Barnier, B., 55; 113; See Penduff, T., 132 Baross, J.A. See Jumars, P.A., 226, 338 Barquero, C. See Campos, E., 579 Barr, H.M. See McKay, W.A., 596 Barragy, E.J., 58; 113 Barrera Guevara, J.C., 245, 284; 294 Barrett, J., 492; 576 Barrett, N.S. See Edgar, G.J., 296 Barrett, S.M. See Volkmann, J.K., 169 Barrett, T., 527; 576 Barros, A.P., 58, 91; 113 Barros, N.B. See Beck, C.A., 554; 577 Barrowman, N.J. See Myers, R.A., 419 Barry, J.B. See DiTullio, G.R., 224 Barry, J.P. See Grebmeier, J.M., 205; 225; See Yoklavich, M.M., 309 Barten, N.L. See Anthony, R.M., 575 Barthel, D., 185, 191, 194, 202, 207; 222 Barthélemy, E. See Guizen, K., 122 Barton, E.D. See Shapiro, G.I., 136 Bartsche, A., 153; 162 Bascheck, B. See Lafuente, J.G., 127 Bashore, T.L., 573; 576 Basova, S. See Leppänen, J.-M., 484 Bastida, R.O. See Rodriguez, D.H., 552; 601 Bastrop, R. See Röhner, M., 487

Bate, G.C. See Adams, J.B., 256, 258; 293; See Snow, G.C., 306; See Talbot, M.M.B., 307 Bates, C.R. See Bates, M.R., 577 Bates, M.R., 496; 577 Bates, P.W. See Mallin, M.A., 302 Bathmann, U., 185, 205; 222 Bathmann, U.V. See Gleitz, M., 224 Batten, S.D. See Duineveld, G.C.A., 224 Battista, T.A. See Livingston, R.J., 301 Bauchop, T., 564; 577 Bauer, J.E. See Wolgast, D.M., 232 Baugh, T.M., 286; 294 Baumann, M.E.M. See Thomas, D.N., 168 Baumert, H., 51, 75; 113; See Burchard, H., 76; 115; See Lane, A., 127; See Nöhren, I., 131 Bay, J. See Bagge, O., 479 Bayne, B.L. See Harris, J.R.W., 123 Bayne, G.L.S. See Copeland, G.J.M., 43, 103; 116 Bay-Schmith, E., 353; 411 Bazely, D.R., 559; 577 Bazhin, A.G., 374, 375; 411; See Andrew, N.L., 343– 425 Beal, B.F. See Seward, L.C.N., 422; See Vadas, R.L., 361; 423, 424 Beardall, J. See Mitchell, C., 149, 150; 166 Beardsley, R.C. See Signell, R.P., 136 Beaulieu, S., 210; 222 Beaulieu, S.E., 171–232; 172, 181, 190, 193, 195, 206, 207, 211, 215, 216; 222; See Smith Jr, K.L., 230 Beck, C.A., 554; 577 Becker, B.L. See Hiruki, L.M., 589 Becker, G. See Berlamont, J., 114 Becker, G.A., 89, 104; 113 Becker, P.R., 567; 577 Beckers, J.-M., 54, 55, 60, 62; 113; See Brasseur, P., 115; See Grégoire, M., 122; See Stanev, E.V., 63; 137; See Deleersnijder, E., 63; 119; See Delhez, E.J.M., 119 Beckley, L.E. See Potter, I.C., 304 Beckmann, A. See Haidvogel, D.B., 42, 44, 45, 48, 59, 70; 122 Beckmen, K.B., 556; 577 Becq, F. See Benoit, M., 113 Beddard, F.E., 508; 577 Beddig, S. See Pohlmann, T., 133 Beeftink, W.G., 501, 502, 503; 577 Beena, K.R. See Arun, A.B., 576 Beever, E.A., 573; 577

612

AUTHOR I ND E X

Behan-Pelletier, V.M. See Brussaard, L., 335 Behrenfeld, M.J., 333; 335 Beier, J.A., 181, 200, 209; 222 Beja, P.R., 539; 577 Belem, A.L. See Mock, T., 166 Bell, G. See Cobley, N.D., 549; 580 Bell, J.D., 283; 294; See Bishop, K.A., 241, 281; 294 Bell, M.J., 44, 51, 71, 88; 113 Ben-David, M., 545; 577 Bender, E.A., 243; 294 Bengtsson, W. See Graf, G., 225 Benke, A.C., 239; 294 Benner, R. See Amon, R.M.W., 162 Bennet, E.L., 500, 564; 577 Bennett, A.F. See Le Provost, C., 31 Bennett, B.A., 313; 335 Bennett, P.M. See Kuiken, T., 593 Bennett, W.A. See Rogers-Bennett, L., 421 Bennun, L.A. See Waiyaki, E., 570, 572; 606 Benoit, M., 100; 113 Benson, S. See Scholin, C.A., 602 Bentley, N. See Andrew, N.L., 410 Berelson, W. See Smith, C.R., 230; See Tengberg, A., 231 Berentsen, C.W.J. See Gerritsen, H., 93; 121 Beresford, N.A. See Howard, B.J., 590 Berg, P. See Vested, H.J., 139 Berg, T.B. See Sittler, B., 602 Bergamasco, A., 74, 98; 114; See Boscolo, R., 98; 114; See Umgiesser, G., 57; 139 Berge, J.A., 467; 479 Berger, J. See Keiper, R.R., 556, 561; 591 Bergerud, A.T., 496; 577 Berggren, P. See Connor, R.C., 580 Berghuis, E.M. See de Wilde, P.A.W.J., 223; See Duineveld, G.C.A., 224; See Witbaard, R., 232 Berglund, J., 437; 479 Berlamont, J., 107; 114 Bernard, F.R., 386, 403, 405; 411 Bernhoft, A. See Skaare, J.U., 603 Bernier, S. See Robinson, S.M.C., 421 Berntsen, J., 38, 43, 63, 77; 114; See Aksnes, D.L., 111 Berrow, S.D., 527; 577 Berry, A.J., 513; 577 Berry, R.J., 495, 522, 526; 577 Bert, T.M. See Travis, J., 423 Berteaux, D., 494; 577 Bertellotti, M. See Blanco, G., 578

Bertram, D.F., 523; 577; See Blight, L.K., 578; See Drever, M.C., 583 Bertram, V., 103; 114 Bervas, J.Y. See Desbruyères, D., 336 Berzinsh, V., 442, 443; 479 Best, R.C., 553; 577 Bester, M.N. See Bloomer, J.P., 536; 578 Bett, B.J., 175, 177, 187, 190, 207, 211, 217; 222; 313, 328; 335; See Gooday, A.J., 336; See Lampitt, R.S., 227; See Rice, A.L., 229 Bettadpur, S. See Eanes, R.J., 24; 29 Betteridge, K. See Sutherland, R.D., 604 Beuchat, C.A., 541; 577 Beucher, O. See van der Wal, R., 606 Bevanger, K., 537; 577 Beveridge, G., 520; 577 Beyer, F., 467; 479 Bickert, T. See Mackensen, A., 227 Bidle, K.D., 156; 162 Bidlot, J.R., 99, 106; 114 Biebach, H. See Limberger, D., 594 Bienfang, P.K. See Bruland, K.W., 223 Bigazzi, M., 494; 577 Bigg, G.R. See Young, E.F., 141 Bignal, E., 543; 577 Bignal, E.M. See McCracken, D.I., 573; 595 Bignell, D.E. See Brussaard, L., 335 Bijvelds, M.D.J.P., 49, 78; 114 Billen, G., 97; 114 Billett, D.S.M., 172, 175, 188, 189, 199, 200, 203, 207, 216; 222; 313; 335; See Rice, A.L., 229, 339 Bills, P. See Matthews, K., 129 Bilton, D.T. See Attrill, M.J., 576 Bingham, B.L. See Kathiresan, K., 513, 570; 591 Birch, P.B. See Gordon, D.M., 297 Birkhead, T.R., 531; 578 Birks, J.D.S., 521, 543, 544; 578; See Dunstone, N., 543; 583 Biscoito, M. See Zino, F., 608 Bishop, J.K.B. See Bruland, K.W., 223 Bishop, K.A., 241, 281; 294 Bisogni, J.J. See Driscoll, C.T., 296 Bjork, G., 59; 114 Bjork, R.D. See McIvor, C.C., 302 Blaber, S.J.M., 283, 284; 294; See Cyrus, D.P., 284; 296 Black, J.M. See Owen, M., 497; 598 Black, K., 177, 188, 199, 207, 212, 214, 219; 222; See Amos, C.L., 222

613

AUTHOR I ND E X

Black, M.M.D. See Stoecker, D.K., 168 Blackadar, A.K., 76; 114 Blackburn, S.I. See Volkmann, J.K., 169 Blackburn, T.H. See Hansen, L.S., 208; 225; See Snelgrove, P.V.R., 341 Blackford, J. See Allen, J.I., 111 Blackford, J.C. See Allen, J.I., 111 Blair, N. See Levin, L., 227; See Schaff, T., 340 Blair, N.E., 215, 218, 219; 222; See Levin, L.A., 227 Blake, J.A., 316; 335 Blanc, F. See Leveau, M., 301 Blanco, G., 492, 546; 578 Bland, K.P. See Leus, K., 593 Blanton, J. See Haidvogel, D.B., 123 Blasi, C. See Acosta, A., 575 Blaume, F. See Fohrmann, H., 121 Bleck, R., 43, 64; 114; See Brydon, D., 115; See Vigan, X., 139 Blight, L.K., 525; 578; See Drever, M.C., 583 Blijleven, H.J. See Spaans, B., 603 Blinet, P. See Terlouw, E.M.C., 605 Blixenkronemoller, M. See Barrett, T., 576 Blomqvist, E.M., 472; 479; See Bonsdorff, E., 437; 479; See Östman, M., 436, 435; 485 Blomqvist, M. See Cederwall, H., 433; 480 Blomqvist, S., 190; 223 Blood, E.R. See Gardner, L.R., 586 Bloomer, J.P., 536; 578 Blount, C. See Andrew, N.L., 410 Blumberg, A.F., 47; 114 Blythe, L.L. See Wachenheim, D.E., 606 Bodaly, R.A. See Rosenberg, D.M., 305 Bode, L., 38, 51; 114 Bodkin, J.L., 498, 527; 578 Boehme, S. See Schaff, T., 340 Boersma, P.D. See Yorio, P., 608 Boetius, A., 212; 223; See Pfannkuche, O., 228; See Witte, U., 232 Boghen, A. See Landry, T., 593 Bohrer, R. See Thunell, R.C., 231 Boissy, A. See Terlouw, E.M.C., 605 Bolanos, H. See Campos, E., 579 Bolding, K. See Burchard, H., 115 Bolland, M.D.A., 504; 578 Bologna, P.A.X., 398; 411 Bolser, R.C., 573; 578 Bolt, P.A. See Buck, K.R., 162 Bolzano, A., 51, 84; 114 Bonardelli, J.C., 393; 411 Bonde, R.K. See Mignucci-Giannoni, A.A., 596

Bonesi, L., 543; 578 Boness, D.J., 543, 549; 578; See Francis, J.M., 552; 586 Böning, C. See Grifﬁes, S.M., 122 Bonnell, M.L., 528; 578 Bonner, W.N., 548; 578; See Berry, R.J., 577 Bonnett, P.J.P. See McKay, W.A., 596 Bonsdorff, E., 427– 489; 431, 435, 436, 437, 438, 473, 475; 479; See Blomqvist, E.M., 472; 479; See Kraufvelin, P., 483; See Leppäkoski, E., 431; 484; See Norkko, A., 428, 437; 485; See Norkko, J., 485; See Perus, J., 486; See Rumohr, H., 487 Booij, N., 48, 100; 114; See Ris, R.C., 134 Boon, A.R., 199, 219; 223 Boon, J.G. See Gerritsen, H., 121 Boon, J.P., 567; 578 Boon, P.J., 235, 240, 241; 294 Boorman, D. See Proctor, R., 134 Boorman, L.A., 519, 520; 578 Booth, D. See Koop, K., 300; See Landry, T., 593 Booth, L.H. See Ogilvie, S.G., 598 Booth, W.E. See Choy, S.C., 255; 295 Boots, B.N. See Mallory, F.F., 527; 595 Born, E.W. See Ferguson, S.H., 585 Born, G.H. See Tierney, C.C., 34 Bornemann, H., 514; 578 Borum, I. See Kühl, M., 165 Borum, J. See Bach, S.S., 294; See Terrados, J., 307 Bos, D. See van der Wal, R., 606 Boschker, H.T. See Moodley, L., 228 Boscolo, R., 98; 114 Bosseur, F., 65; 114 Bothma, J.D. See Nel, J.A.J., 597 Botkin, D.B. See Wiens, J.A., 308 Botsford, L.W., 369, 370, 404, 405, 409; 411; See Andrew N.L., 343– 425; See Lundquist, C.J., 418; See Morgan, L.E., 419; See Quinn, J.F., 421; See Smith, B.D., 422; See Wilen, J.E., 425; See Wing, S.R., 425 Boucher, G., 315, 316; 335; See Smith, C.R., 340; See Snelgrove, P.V.R., 340 Boudouresque, C.F., 384, 390, 392; 411; See Le Direac’h, J.P., 418; See Sala, E., 421 Boudra, D. See Bleck, R., 43, 64; 114 Boudreau, B.P. See van der Loeff, M.M.R., 93; 139 Boudreau, P. See Heniche, M., 123 Boulton, A.J. See Kingsford, R.T., 300; See Puckridge, J.T., 304

614

AUTHOR I ND E X

Bourel, B., 520; 578 Bourget, E. See Ardisson, P.-L., 246, 277; 294; See Himmelman, J.H., 415 Bourke, R.H. See Wilson, O.B., 607 Bourman, R.P., 242, 281; 294 Bousses, P. See Chapuis, J.L., 580 Boutier, B. See Pham, M.K., 133 Boutillier, J.A. See Perry, R.I., 420 Boutle, C. See Newton, P.P., 228 Bouvier, J. See Guinet, C., 528; 588 Bouwer, H., 567; 578 Bove, J.M. See Konai, M., 592 Bove, L.L. See Ovsyanikov, N.G., 598 Boveng, P.L. See Hiruki, L.M., 589 Bowen, K.G. See Myers, R.A., 419 Bowen, L. See Crooks, K.R., 581 Bowen, P. See Abal, E.G., 293 Bowen, W.D., 492, 493, 554, 572; 578 Bowman, J. See Nichols, D.S., 166 Bowyer, R.T., 539, 568; 578; See Ben-David, M., 577; See Duffy, L.K., 583 Boxall, S.R. See Ng, B., 131 Boyce, W.M. See Sweitzer, R.A., 604 Boyd, I.L. See Boyd, J.M., 506, 538; 578; See Fedak, M.A., 585 Boyd, J.M., 536, 538; 578; See Williamson, K., 501, 511, 522, 548; 607 Boye, P., 512; 578 Boyer, K.E. See Fong, P., 297 Boynton, W. See Jonge, V.N. de, 483 Boynton, W.R., 237, 267; 294 Bozinovic, F., 498; 578 Brabyn, M., 528; 579 Bradbury, A., 387; 411; See Andrew, N.L., 343– 425; See Ebert, T.A., 414; See Lai, H.L., 386, 387, 405; 417; See Pﬁster, C.A., 386, 387, 405; 420 Bradshaw, C.J.A., 551; 579 Braine, S. See Avery, G., 576 Branco, J.V. See Gava, A., 587 Brandt, P., 53, 55, 102; 114 Brandt, R. See Rasmussen, E., 134 Brankart, J.M., 104; 115; See Brasseur, P., 115 Branson, B.A., 553, 574; 579 Branson, M.L. See Branson, B.A., 553, 574; 579 Brasseur, P., 58; 115; See Beckers, J.-M., 113; See Brankart, J.M., 104; 115; See DenisKaraﬁstan, A., 119 Brattberg, G., 452, 453; 479 Brattegard, T. See Svavarsson, J., 341

Brawley, J. See Valiela, I., 307 Bray, N.A., 21; 28 Breault, A.M., 543; 579 Breen, C.M. See Quinn, N.W., 304 Breen, P.A., 363, 381, 386, 403; 411, 412; See Mann, K.H., 401; 418 Bregnballe, T. See Madsen, J., 594 Breitburg, D.L., 285; 295; 477; 479 Breivik, L.-A., 105; 115 Breivik, Ø., 103, 107; 115 Bremner, J.M., 188; 223 Brenchley, J.E. See Sheridan, P.P., 144, 157, 161; 168 Brenchley, J.L., 257, 258; 295 Brenner, R.R. See Nichols, D.S., 166 Brenon, I., 48; 115 Breton, M., 73; 115 Breton, M. See Salomon, J.C., 135 Brett, P.A. See Andrew, N.L., 410; See Byrne, M., 412 Brewer, D.T. See Blaber, S.J.M., 294 Brey, T. See Iken, K., 226 Brezinski, M.A. See Martin-Jézéquel, V., 166 Brierley, A.S., 144, 145, 150, 158; 165 Briggs, J. See Wyer, M.D., 608 Briggs, S.V. See Gosper, D.G., 298 Brillant, S.W. See Terhune, J.M., 507; 605 Brindley, E. See Norris, K., 597 Brink, K.H., 43, 69; 115 Brinkhaus, A.J. See Waithman, J.D., 606 Britschgi, T. See Giovannoni, S., 336 Brittnacher, J.G. See Botsford, L.W., 411 Broadbent, A. See Koop, K., 300 Brocchini, M. See Zecchetto, S., 141 Brock, M.A., 286; 295 Brockmann, U. See Pohlmann, T., 133; See Puls, W., 134 Brockmann, U.H. See Dick, S., 119 Brodie, J. See Koop, K., 300 Broker, I. See Nicholson, J., 131 Brook, I.M., 251; 295 Brooke, M.D. See Pain, D.J., 598 Brooke, R.K. See Cooper, J., 521; 581 Broomﬁeld, K., 517; 579 Brosens, L. See Jauniaux, T., 590 Broström, G., 79; 115 Browder, J.A., 277; 295; See Sklar, F.H., 253, 254; 306 Brown, C.F. See Norris, K., 597 Brown, C.L. See Lambshead, P.J.D., 338 Brown, D. See Craik, J.C.A., 545; 581

615

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Brown, J. See Hill, A.E., 123; See Young, E.F., 141 Brown, L., 532, 533; 579; See MacDonald, D.W., 594 Brown, M.P. See Mohan, R.K., 130 Brown, P.J., 492; 579 Brown, P.S., 172; 223 Brown, R.W. See Lawrence, M.J., 545, 546, 566; 593 Brown, T.E., 284; 295 Brown, V.K. See Brussaard, L., 335 Brown, W.K., 500; 579 Brown, W.Y., 521; 579 Brownell, R.L., 541; 579 Bruce, L. See Moller, H., 596 Bruland, K.W., 200; 223 Brumsickle, S.J. See Smith, C.R., 320; 340 Brunet, S., 71; 115 Bruno, M., 65; 115 Brunton, M. See Kueh, C.S.W., 593 Brussaard, L., 318, 319; 335 Brussard, P.F. See Beever, E.A., 573; 577 Bruton, M.N., 284, 286; 295 Bryan, F. See Wahr, J., 34 Bryan, F.O. See Grifﬁes, S.M., 122; See Hecht, M.W., 123 Bryan, K., 43, 65; 115; See Treguier, A.M., 139 Bryant, V. See McClusky, D.S., 302 Bryden, H.L. See Tsimplis, M.N., 21; 34 Bryden, M.M. See Rogers, T.L., 552; 601 Brydon, D., 91; 115 Brylinski, J.-M. See Sourna, A., 306 Bucher, D., 239; 295; See Koop, K., 300 Buchholz, W. See Rosenthal, W., 135 Büchmann, B., 101; 115 Buck, G.B. See Quinn, T.P., 546, 547; 600 Buck, J.D. See Frasca, S., 586 Buck, K.R., 156; 162; See Garrison, D.L., 147, 152, 153; 163 Buckingham, C.A., 492, 574; 579 Buckle, A. See Zino, F., 608 Bückle, F., 253; 412 Buckley, F.G., 522; 579 Buckley, P.A. See Buckley, F.G., 522; 579 Buckley, P.T. See Sturges, W.T., 168 Buden, D.W. See Wiles, G.J., 607 Budgen, G.L. See Sinclair, M., 306 Buessler, K.O. See Staneva, J.V., 137 Bukata, R.P. See Pozdnyakov, D.V., 133 Bukhanovskii, A.V. See Rozhkov, V.A., 135 Bullock, I.D., 509; 579

Bulmer, A.C., 492; 579 Bulmer, G.S. See Bulmer, A.C., 492; 579 Bunn, S.E. See Loneragan, N.R., 301 Burbidge, A.A., 495; 579 Burchard, H., 45, 63, 67, 76, 77; 115; See Beckers, J.-M., 113 Burdick, D.M. See Short, F.T., 305 Bureau, D. See Campbell, A., 412 Burgers, G. See de las Heras, M.M., 118 Burke, M.G., 500; 579 Burkholder, J.M. See Glasgow, H.B., 243; 297 Burkill, P. See Archer, S.D., 162 Burness, G.P., 545; 579 Burnett, W.C. See Eckman, J.E., 336 Burnham, M.P. See Lampitt, R.S., 227 Burns, F. See Van Wagenen, R.F., 606 Burton, J.D. See Tappin, A.D., 138 Burton, R.W. See Summers, C.F., 604 Busalacchi, A.J. See McPhaden., 31; See Picaut, J., 15; 32 Buschmann, U. See Haas, C., 164 Busman, M. See Scholin, C.A., 602 Bussell, J.A. See Thompson, R.C., 605 Bussmann, I. See Eicken, H., 163 Bustamante, R., 352; 412 Bustos, E., 406; 412 Butler, J.R.A., 542; 579 Buttolph, P. See Kalvass, P., 416 Buzas, M.A. See Culver, S.J., 317; 335 Byrd, G.V., 530; 579 Byrne, M., 390, 391; 412; See Andrew, N.L., 390; 410; See Conand, C., 346, 382; 413; See King, C.K., 417 Byun, S.A., 496; 579 Cabello, C.C., 541; 579 Cabot, D., 532; 579 Cabrer, B. See Rossello, M.A., 601 Caddy, J.F., 376; 412; 477; 480 Cadee, G.C., 194; 223; 271; 295 Cadman, A., 521, 566; 579 Cadwallader, P., 241, 242, 281; 295 Cahet, G., 175, 213, 214; 223 Caillez, J.-C. See Bourel, B., 578 Cailliet, G.M. See Yoklavich, M.M., 309 CALCOFI, 373; 412 Caldwell, D.R., 69; 115 Caldwell, P. See Kilonsky, B.J., 4; 31 Callahan, A.E. See Oguz, T., 131 Callahan, P.S. See Chelton, D.B., 29 Callinan, R.B. See Sammut, J., 305

616

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Calman, J. See Robinson, A.R., 229 Calvin, J. See Ricketts, E.F., 493, 498, 533; 601 Cambridge, M.L., 263; 295; See McComb, A.J., 302 Cameron, J. See Silva, M., 602 Cameron, M., 567; 579 Cameron, R.A., 386; 412 Campbell, A., 363, 365; 412; See Andrew, N.L., 343– 425; See Jamieson, G.S., 408; 416; See Perry, R.I., 420 Campbell, B. See Craik, J.C.A., 544; 581 Campbell, R. See McClusky, D.S., 302 Campin, J.M. See Beckers, J.-M., 113 Campin, J.-M. See Deleersnijder, E., 119 Campos, E., 498; 579 Campos-Creasey, L.S., 216; 223 Canbolat, A.F. See MacDonald, D.W., 594 Canceil, P. See Le Provost, C., 31 Candela, J., 52; 116; See Lafuente, J.G., 127 Cane, M.A., 86; 116 Canﬁeld, D.E., 218; 223 Canizares, R. See Babaric, V., 112 Capone, D. See Koop, K., 300 Capotondi, A. See Signell, R.P., 136 Caputi, N. See Loneragan, N.R., 301 Carbajal, N., 78; 116 Cardeti, G. See Diguardo, G., 582 Cardinal, A. See Himmelman, J.H., 415 Carle, P. See Konai, M., 592 Carlile, N. See Priddel, D., 600 Carlini, A. See Bornemann, H., 578 Carlson, C.A., 156, 157; 162 Carlsson, M., 66; 116 Carlucci, A.F. See Smith Jr, K.L., 230; See Wolgast, D.M., 232 Carman, R. See Jonsson, P., 444, 445, 476; 482 Carner, S. See Palacín, C., 420 Carney, R.S., 210, 211, 218; 223; 312, 327; 335 Carniel, S. See Bergamasco, A., 114 Carnochan, S. See Moore, P., 32 Carolsfeld, W. See Breen, P.A., 411 Carpenter, S.D. See Smith, C.R., 340 Carpenter, S.M. See Gosper, D.G., 298 Carrada, G.C. See Guglielmo, L., 164 Carranza, J. See Fernández-Llario, P., 585 Carretero, J.C. See Alvarez Fanjul, E., 112 Carreto, J.-C. See Monbaliu, J., 130 Carriquiry, J.D., 245; 295 Carroll, D.S. See Dowler, R.C., 583 Carss, D.N. See Kruuk, H., 538, 539, 540; 592

Cartwright, D.E., 18, 23; 28; 45, 69; 116; See Le Provost, C., 31 Case, T.J. See Bender, E.A., 294 Casitas, S. See Auclair, F., 112 Castel, J., 267; 295 Castellanos, A. See Alvarez-Cardenas, S., 575 Castellari, S., 80, 81; 116 Castilla, J.C., 349, 352, 397, 406; 412; See Botsford, L.W., 411; See Bustamante, R., 352; 412; See Duran, L.R., 352, 397; 413; See González, L., 415; See Guisado, C., 406; 415; See Navarette, S.A., 493, 510, 524, 525; 597; See Vásques, J.A., 424 Castillo, C., 512; 580 Castley, J.G. See Kerley, G.I.H., 592 Castroviejo, J. See Vila, C., 606 Casulli, V., 49, 55; 116 Catalano, G. See Guglielmo, L., 164 Cato, I. See Rosenberg, R., 487; See Smith, S., 488 Cattaneo-Vietti, R. See Pusceddu, A., 167 Caudron, A. See Twiss, S.D., 605 Caumette, P. See Castel, J., 295 Causon, D.M. See Hu, K., 124; See Mingham, C.G., 130 Cavaleri, L. See Komen, G.J., 126 Cavallini, P., 535; 580 Cayan, D.R. See McGowan, J.A., 358 Caye, G., 257, 258; 295 Cazenave, A., 26; 28; See Fu, L.-L., 4; 29; See Raper, S.C.B., 33 Ceballos, G., 578; 580 Cedenilla, M.A. See Gazo, M., 587 Cederwall, H., 433, 443, 444, 448, 454, 455, 472, 473; 480; See Andersin, A.-B., 478; See Andersson, L., 478 aetina, M., 47, 58; 116 Chadderton, W.L. See Villouta, E., 424 Chadwick, A.J. See Ozanne, F., 132 Chai, P.P.K. See Salter, R.E., 601 Chalikov, D. See Tolman, H.L., 100; 138 Challenor, P.G., 19; 28 Chambers, C. See Fedak, M.A., 585 Chambers, D.P., 17; 28 Chan, E.S. See Tkalich, P., 496; 605 Chan, H.M., 567; 580; See Kuhnlein, H.V., 593 Chan, S.L. See Varanasi, U., 606 Changming, L., 245; 295 Chanin, P., 541, 542; 580; See Linn, I., 545; 594 Chao, B.F. See O’Connor, W.P., 32

617

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Chao, S.-Y. See Paluszkiewicz, T., 132 Chao, Y. See Song, Y.T., 62; 137 Chapalain, G., 67; 116; See Baumert, H., 113 Chapdelaine, G. See Donaldson, G.M., 583 Chapman, A.R.O., 381, 401; 412 Chapman, A.S., 382; 412 Chapman, D.C., 87; 116; See Brink, K.H., 43, 69; 115 Chapman, M.G., 254; 295 Chapman, V.J., 254; 295 Chapron, B. See Kudryavtsev, V.N., 126 Chapuis, J.L., 494, 536; 580 Charlier, G. See Jauniaux, T., 591 Charmantier, G. See Bambang, Y., 294 Charnock, H., 77, 101; 116 Chartier, M. See Madec, G., 129 Chase, M.K., 571; 580 Chase, M.R., 327; 335 Chase, S.C. See Gordon, D.M., 298 Chassignet, E.P., 43, 51, 109; 116; See Grifﬁes, S.M., 122 Chatto, R., 527, 528, 529; 580 Chaubey, I. See Benke, A.C., 294 Chavanich, S. See Harris, L.G., 415 Chave, A.D., 20; 28; See Filloux, J.H., 29; See Luther, D.S., 31 Chavez, C. See Ceballos, G., 580 Chavez, F.P. See Scholin, C.A., 602 Chawla, A., 53; 116 Chelton, D.B., 17; 29 Chen, F., 77, 94, 98; 116; See Xing, J., 140 Chen, Y., 71; 116 Cheney, R., See McPhaden, M.J., 31; See Mitchum, G.T., 32 Cheng, K.M. See Breault, A.M., 543; 579; See Howald, G.R., 590 Cherrier, R. See Faye, B., 584 Chesher, T. See Péchon, P., 132 Cheshire, A.C. See Collings, G.J., 266; 295 Chester, C.M. See Balch, T., 410; See Harris, L.G., 405; 415 Chester, E.T. See Robson, B.J., 305 Cheung, K.F. See Büchmann, B., 115 Chia, F.-S. See Levitan, D.R., 418 Chiarello, A.G., 570; 580 Chick, R.C. See Andrew, N.L., 410 Chifﬁngs, A.W. See Silberstein, K., 306 Childers, D., 240; 295 Childers, S.E. See Levin, L.A., 338 Chimera, C., 493; 580 Chiswell, S.M. See Mitchum, G.T., 25; 32

Chong, J. See Bray, N.A., 28 Chotkowski, M. See Horn, M.H., 589 Choy, S.C., 255; 295 Chretiennot-Dinet, M.-J. See Riaux-Gobin, C., 229 Christensen, H., 177; 223; See Kanneworff, E., 177, 190; 226 Christensen, J.D. See Livingston, R.J., 301 Chronis, G. See Nittis, K., 131; See Soukissian, O., 137 Church, J. See Siedler, G., 33 Church, J.A., 16, 22; 29 Chyba, C.F., 144; 162 Cinelli, F. See Airoldi, L., 265; 293 Clarisse, S. See Hoffmann, L., 415 Clark, R.C. See Varanasi, U., 606 Clark, R.G., 557; 580; See Sargison, N.D., 602 Clark, T.B. See Konai, M., 592 Clarke, A. See Cripps, G.C., 159; 162 Clarke, A.J., 21; 29; See Hong, B.G., 30; See Sturges, W., 34 Clarke, K.R., 292; 295 Clarke, K.R. See Warwick, R.M., 319, 320; 341 Clarke, M.F. See Bolland, M.D.A., 578 Clarke, S., 92; 116 Clément, A., 406; 412 Clifford, M. See Horton, C., 124 Clifton, J., 94; 116 Clode, D., 545; 580 Cloern, J.E. See Jassby, A.D., 299; See Lucas, L.V., 301 Cloonan, M.J. See Vale, T.G., 605 Close, A.R. See Garrison, D.L., 163 Clough, B.F., 513; 580 Clutton-Brock, T.H., 560; 580; See Pemberton, J.M., 599 Coantic, M. See Verdier-Bonnet, C., 139 Coblentz, B.E. See Keegan, D.R., 591 Cobley, N.D., 549; 580 Coelho, L.H.L., 496; 580 Coello, J.J. See Castillo, C., 580 Coen, L.D. See Heck, K.L., 298 Cohen, Y. See Alldredge, A.L., 208; 222 Cohn, J.P., 508; 580 Coignoul, F. See Jauniaux, T., 590 Colagross, A. See Gulland, F.M.D., 588 Colby, D.R. See Govoni, J.J., 298 Cole, A.R. See Harris, P.T., 298 Colella, P., 71; 116 Coleman, C.D. See Paterson, I.W., 558, 559, 560; 599

618

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Coleman, F.C. See Travis, J., 423 Coleman, J.M. See Wright, L.D., 252; 309 Coleman, M.C. See Chimera, C., 580 Coles, R.G. See Preen, A.R., 304 Colijn, F. See Berlamont, J., 114 Coll, J. See Koop, K., 300 Collier, R. See Honjo, S., 225 Collings, G.J., 266; 295 Collins, C.T. See Kelsey, R., 493; 591 Collins, M.B. See O’Connor, B.A., 131; See Tsimplis, M.N., 139 Collister, J. See Thunell, R.C., 231 Coltman, D.W., 495; 580 Colwell, M.A., 492, 493; 580 Coma, R. See Sala, E., 421 Conacher, C.A. See Peterken, C.J., 555; 599 Conand, C., 346, 382, 392; 413 Conley, D.J., 462; 480 Connelly, D. See Barnett, P.R.O., 222 Connolly, R.M., 254; 295 Connor, J.J. See Westerink, J.J., 140 Connor, R.C., 529; 580 Conover, D. See Travis, J., 423 Conover, M.R. See Hoover, S.E., 517; 589 Conradt, L., 561; 580 Conroy, C.J., 496; 580 Conroy, J.W.H. See Kruuk, H., 592 Conte, M.H., 171, 179, 187, 194, 199, 201, 203, 204, 206, 210, 212, 218, 219; 223 Conway, E., 557; 580 Cook, J.A. See Conroy, C.J., 496; 580 Cook, T. See Norris, K., 597 Cooke, B.D. See Myers, K., 597 Cooke, J.C. See Frasca, S., 586 Cooper, A.B., 404; 413 Cooper, J., 521; 581 Copeland, G.J.M., 43, 103; 116 Copson, G., 521; 581 Corcoran, E.F. See Kimball Jr, J.F., 226 Cordaro, J. See Scholin, C.A., 602 Córdoba, L. See Bückle, F., 412 Cormaci, M., 401; 413 Corniero Lera, M.A. See Giménez-Curto, L.A., 78; 122 Cornwell, C. See Thompson, P., 605 Corp, N., 525, 572; 581; See Raffaelli, D.G., 600 Corton, X. See Hoang, S., 123 Cosson, N. See Paterson, G.L.J., 339 Cosson-Sarradin, N., 326; 335 Costa, D.P. See Kretzmann, M.B., 592; See Worthy, G.A.J., 607

Costa, J. See Valiela, I., 307 Costall, D.A. See Sutherland, R.D., 604 Costanza, R., 293; 295 Costley, K. See Fitt, W.K., 272; 297 Cota, G.F. See Priscu, J.C., 167; See Sturges, W.T., 168 Cottier, F. See Eicken, H., 163; See Haas, C., 164 Coughran, D.K. See Mawson, P.R., 527, 553; 595 Coukell, A., 553; 581 Coulibaly, M. See Barnier, B., 113 Coull, B.C., 253, 275; 296 Coulson, J.C., 549, 553; 581 Coulson, T.N. See Pemberton, J.M., 599 Courchamp, F., 493, 494; 581 Courtier, P. See Vigan, X., 139 Courtney, F.X. See Fonseca, M.S., 585 Courtney, P.A., 521; 581 Covich, A.P. See Palmer, M.A., 339 Coward, A.C. See Meier, H.E.M., 130; See Webb, D.J., 140 Cowen, R.K., 399; 413 Cowie, G.L. See Good, E.I., 224 Cox, C.R. See Boness, D.J., 578 Cox, M.D., 43, 65; 116 Cox, P.A. See Elmquist, T., 584 Cox, R. See Hudson, P., 543; 590 Coyne, G. See Rex, M.A., 339 Craig, A.M. See Wachenheim, D.E., 606 Craig, M.P., 549; 581 Craig, S.F. See Judge, M.L., 274; 299 Craik, J.C.A., 543, 544, 545; 581 Crain, D.A., 567; 581 Crance, C., 574; 581 Cranford, P. See Grant, J., 225 Crawford, D.W. See Wong, C.S., 232 Creaser, E.P., 361, 399; 413; See Andrew, N.L., 343– 425 Crépon, M. See Madec, G., 129 Cretaux, J.-F. See Cazenave, A., 28 Cripps, G.C., 159; 162 Crise, A., 72, 98; 117; See Crispi, G., 117 Crispi, G., 98; 117; See Crise, A., 117 Cristani, J. See Gava, A., 587 Crocker, L.H. See Malone, T.C., 302 Croft, T.J., 78; 117 Cronin, G., 266; 296 Cronin, T.M., 314, 325, 334; 335 Crooks, K.R., 572; 581 Crowther, J. See Wyer, M., 608

619

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Croxall, J.P. See González-Solís, J., 587 Cruzado, A. See Bahamón, N., 113; See Varela, R.A., 139 Csirke, J. See Garcia, S., 414 Culver, S.J., 317; 335 Cummings, V.J., 572; 581 Curchitser, E.N., 58; 117 Currie, D.J. See Fraser, R.H., 323; 336 Curtin, T.B., 246, 247, 249; 296; See Paluszkiewicz, T., 132 Curtis, T.F.G., 501, 503; 581 Cushman-Roisin, B. See Malabib, V., 129 Cutchin, D.L. See Caldwell, D.R., 115 Cutter Jr, G.R. See Bonsdorff, E., 479 Cyrus, D.P., 284; 296 Czitrom, S.P.R., 54; 117 Czytrich, H., 179, 212; 223 Daae, H.L. See Prestrud, P., 600 Daborn, G.R. See Amos, C.L., 222 Dahl, E. See Aure, J., 463; 478; See Johannessen, T., 466; 482 Dahlin, H. See Rosenberg, R., 487 Dahm, C.N. See Molles, M.C., 240; 303 Daiber, F.C., 493, 505; 581 Dailey, M.D., 528; 582; See Bonnell, M.L., 578 Daily, G.C. See Postel, S.L., 304 Dakin, W.J., 492; 582 Dalla Valle, L. See Andreoli, C., 162 Dallas, J.F. See Telfer, S., 604 Dallot, S. See Sourna, A., 306 Dalrymple, R.W. See Harris, P.T., 298 Daly, K., 145; 162 Dalzell, P., 346; 413 Damm, P. See Becker, G.A., 113; See Laane, R.W.P.M., 126 Damm, P.F. See Smith, J.A., 137 Danielssen, D. See Aure, J., 478 Danielssen, D.S. See Laane, R.W.P.M., 126 Danish Hydraulics Institute, 60; 117 Dannevig, L. See Kearney, M.S., 591 Dantart, L. See Palacín, C., 420 d’Arge, R. See Costanza, R., 295 Dargie, T.C.D., 494; 582 Darling, F.F., 548; 582 Darwin, C., 495; 582 Daumas, R. See Cahet, G., 223 Dauvin, J.-C., 275; 296 D’Avanzo, C. See Valiela, I., 307 Davenport, I.J. See Mason, D.C., 129 Davey, M.K. See Février, S., 121

David, J.H.M., 492; 582; See Oosthuizen, W.H., 598 Davies, A.G., 51, 92, 94; 117 Davies, A.M., 49, 51, 63, 64, 65, 68, 69, 74, 75, 76, 77, 78, 83, 85, 93, 94, 97, 99, 101, 103, 104, 108; 117, 118; See Dyke, P.P.G., 51, 120; See Glorioso, P.D., 101; 122; See Jones, J.E., 44, 60, 65, 85, 87; 125; See Lee, J.C., 76, 84; 127; See Lynch, D.R., 51; 128; See Proctor, R., 72, 134; See Xing, J., 63, 65, 67, 70, 71, 72, 76, 77, 83, 85, 87, 91, 103; 140, 141 Davies, J.L., 507, 510, 549, 551, 552; 582 Davies, J.M., 177, 188, 194, 207; 223 Davies, P. See Wasson, B., 308 Davis, A.A. See Fuhrman, J.A., 322; 336 Davis, G.W. See Tapley, B.D., 34 Davis, J.A., 240; 296; See Robson, B.J., 305 Davis, J.B., 498; 582 Davis, J.R. See Banens, R.J., 573; 576 Davy, A.J. See Harris, D., 506; 589 Dawson, A.G. See Raper, S.C.B., 33 Dawson, R.M. See Holler, N.R., 589 Day, J. See Childers, D., 295 Day, J.W. See Ibanez, C., 590 Day, K.R. See Robertson, A., 601 Day Jr, J.W., 501, 503; 582 Dayton, P.K., 185, 194, 205; 223; 323; 336; 368, 398, 399, 402; 413; 493; 582; See Barilotti, D.C., 410; See Tegner, M.J., 366, 370, 397, 398, 401, 403, 405, 406; 423; See Vetter, E.W., 172; 231; 327; 341 Deacon, J., 370; 413 Deane, G.B., 496; 582 Dearborn, J.H. See Ojeda, F.P., 398; 420 de Bovee, R. See Tengberg, A., 231 de Carvalho, G.S. See Granja, H.M., 507; 587 de Cuevas, B.A. See Webb, D.J., 140 Deegan, L.A., 285; 296 Defeo, O. See Castilla, J.C., 412; See Lercari, D., 279; 301 de Groot, R. See Costanza, R., 295 Deigaard, R., 71, 101; 118 Deiongh, H.H., 555; 582 de Jong, D.J. See Kamermans, P., 299 De Jonge, V.N. See de Swart, H.E., 119 Dekeyser, I. See Lellouche, J.-M., 127 de Kok, J.M., 68, 86; 118 de la Cruz, A.A. See Odum, E.P., 172; 228 De Lange, G.J. See Jumars, P.A., 226 de las Heras, M.M., 99, 105; 118

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Delecluse, P. See Février, S., 121; See Madec, G., 129 de le Cruz, A.A. See Hackney, C.T., 505; 588 Deleersnijder, E., 63, 66, 74, 75, 84; 119; See Beckers, J.-M., 113; See Davies, A.M., 118; See Luyten, P.J., 128; See Mathieu, P.-P., 62; 129; See Ruddick K.G., 135; See Tartinville, B., 138 De Leeuw, J. See Bakker, J.P., 576 de Leeuw, J.W. See Duineveld, G.C.A., 224 de Lestang, S. See Young, G.C., 309 Delft Hydraulics, 44; 119 Delhez, E.J.M., 45, 74, 78, 98; 119; See Deleersnijder, E., 119 Délia, C.F. See Jonge, V.N. de, 483 Delibes, M. See Gaona, P., 586 Dell’Anno, A. See Guglielmo, L., 164 De Long, R. See Scholin, C.A., 602 De Long, R.L., 568; 582 Demarchi, M.W., 560; 582 DeMartini, J. See Kalvass, P., 416 DeMaster, D. See Levin, L., 227; See Schaff, T., 340; See Blair, N.E., 222; See Fornes, W.L., 224; See Levin, L.A., 227, 338; See Miller, R.J., 228; See Smith, C.R., 230, 340 DeMaster, D.P. See Gerber, L.R., 414 Demel, K., 447, 448, 449, 450; 480 DeMeo-Anderson, B. See Valiela, I., 307 De Mey, P. See Echevin, V., 120; See Hoang, S., 123 Deming, J. See Krembs, C., 165 Deming, J.W. See Desbruyères, D., 336; See Jumars, P.A., 226, 338; See Smith, C.R., 340 De Miranda, A.P. See Barnier, B., 113 Demoulin, V. See Hoffmann, L., 415 Denbo, D.W. See Soreide, N.N., 33 Denis, C. See Denis-Karaﬁstan, A., 119 Denis-Karaﬁstan, A., 58; 119 Dennison, W. See Koop, K., 300 Dennison, W.C. See Abal, E.G., 263; 293; See Longstaff, B.J., 259, 262; 301; See Udy, J.W., 261; 307 Derber, J. See Ji, M., 30 Derocher, A. See Skaare, J.U., 603 Derocher, A.E., 547; 582 Derout, D. See Bourel, B., 578 Desbruyères, D., 330, 331; 336 Descals, E. See Rossello, M.A., 601 Descoins, C. See Arnould, C., 575 Descolas-Gros, C. See Riaux-Gobin, C., 181; 229

Des Marais, D.J. See Rau, G.H., 167 Desmecht, M. See Jauniaux, T., 591 Desmond, J.S. See Fong, P., 297 de Swart, H.E., 74; 119; See Schuttelaars, H.M., 136 de Szoeke, R.A., 55; 119 Dethier, M.N. See Hammerstrom, K., 588; See Steneck, R.S., 398; 423 Dethleff, D. See Haas, C., 164; See Harms, I.H., 123 Detienne, X. See Hoffmann, L., 415 Detling, J.K. See Painter, E.L., 598 Dettman, D.L. See Rodriguez, C.A., 305 Deuser, W.G., 313; 336; See Siegel, D.A., 206; 230 Devenon, J.-L. See Lellouche, J.-M., 127 de Verdière, A.C. See Penduff, T., 132 De Villiers, D.J., 551, 552; 582 De Vogelaere, A. See Scholin, C.A., 602 de Vriend, H.J., 93; 119 DeVries, A.L. See Raymond, J.A., 167 de Vries, H. See Gerritsen, H., 122 De Vries, Y. See Bakker, J.P., 502; 576 Dewailly, E., 567; 582 Dewees, C.M. See Rogers-Bennett, L., 421 de Wilde, P.A.W.J., 173, 175, 187, 190, 192, 194, 199, 200, 209, 212, 216; 223; See Duineveld, G.C.A., 224 De Wolde, J. See Raper, S.C.B., 33 DFO, 364, 373, 374, 379, 381, 407; 413 d’Hières, G.C. See Aelbrecht, D., 111 Dhondt, A.A. See Leus, K., 593 Dias, J.A. See O’Connor, B.A., 131 Diaz, G. See Campos, E., 579 Diaz, R.J., 471, 472, 473, 476, 477; 480; See Bonsdorff, E., 479; See Rosenberg, R., 446, 452, 453, 468, 476; 487 DiBacco, C. See Levin, L.A., 330, 331; 338 Dick, S., 43, 73, 74; 119; See Pohlmann, T., 133 Dick, T.M., 225; 296 Dickson, B., 20; 29 Didden, W. See Brussaard, L., 335 Dieckmann, G. See Gutt, J., 225 Dieckmann, G.S., 147, 149, 152, 153; 162; See Arrigo, K.R., 162; See Eicken, H., 163; See Gleitz, M., 164, 164; See Günther, S., 147, 153, 156, 161; 164; See Herborg, L.-M., 164; See Horner, R., 164; See Kennedy, H., 165; See Lange, M.A., 165; See Legendre, L., 165; See Mock, T., 166; See Schnack-Schiel, S.B.,

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168; See Thomas, D.N., 143–169; 144, 148; 168; See Weissenberger, J., 169 Diehl, J.M. See Lundquist, C.J., 418; See Morgan, L.E., 419 Diercks, A., 206; 224 Diesner, U. See Graf, G., 225 Dietrich, D. See Chassignet, E.P., 116 Dietrich, D.E., 44, 66; 119; See Sheng, J., 136; See Zuur, E.A.H., 48, 66; 141 Digby, S.A., 574; 582 Diguardo, G., 527; 582; See Barrett, T., 576 Dijkema, K.S. See Bakker, J.P., 576; See Esselink, P., 584 Di Martino, B. See Bosseur, F., 114 Dinet, A. See Desbruyères, D., 336 Dippner, J.W., 45, 86; 119 Dittman, A.H. See Quinn, T.P., 304 DiTullio, G., 185, 205; 224 DiTullio, G.R., 150; 162 Dix, T.G., 389; 413 Dixon, J.D., 382, 395, 397; 413; See Andrew, N.L., 343– 425; See Ebert, T.A., 414; See Schroeter, S.C., 422 Dixon, K.R., 254; 296 Dixon, M. See Möller, I., 130 Djegham, M. See El Bahri, L., 584 DMU, 451, 456, 457, 461, 462, 463, 468; 480 Doak, D.F. See Estes, J.A., 414, 584 Doan, S.E. See Hoover, D.J., 337; See Smith, C.R., 230, 340 Dobbs, F.C. See Smith, C.R., 230, 340 Dobrowolski, K.A., 497; 582 Dodd, S.L. See Colwell, M.A., 492; 493; 580 Dodson, J.J. See Laprise, R., 275; 300 Doerffer, R. See Pohlmann, T., 133; See Puls, W., 134 Doherty, P.J., 393; 413 Dohi, H., 567; 582 Domburg, P., 566; 582 Domingo, M. See Barrett, T., 576 Dominh, K. See Cazenave, A., 28 Domning, D.P., 556; 582 Donaldson, G.M., 547, 548; 583 Donato, A.N., 103; 119 Doncaster, C.P. See Ward, J.F., 606 Donelan, M. See Komen, G.J., 126 Doney, S.C. See Large, W.G., 127 Dongey, J.-R. See McPhaden, M.J., 31 Dooley, A.L. See Klimstra, W.D., 500; 592 Doos, K. See Meier, H.E.M., 130 Dorandeu, J., 24; 29

Dorfman, E.J., 286; 296 Dorman, L.M. See McGowan, J.A., 338 Dormann, C.F., 502, 521; 583 Dortch, Q. See Rabalais, N.N., 304 Doscher, R. See Meier, H.E.M., 130 Dotsenko, S.F., 102; 119 Doucette, G.J. See Scholin, C.A., 602 Dowding, J.E., 493; 583 Dowell, S.L., 13; 29; See Rickards, L.J., 13; 33 Dowler, R.C., 524; 583 Dowling, T. See Seward, L.C.N., 422; See Vadas, R.L., 423; 424 Downes, M.T. See Priscu, J.C., 167 Downing, B.M. See Ranwell, D.S., 492; 600 Drabkova, V. See Leppänen, J.-M., 484 Draganik, B. See Falandysz, J., 481 Dragosits, U. See Sutton, M.A., 604 Drakopoulos, P. See Tsimplis, M.N., 139 Drakopoulos, P.G., 81, 104; 120; See Stratford, K., 138 Draper, D. See Crance, C., 574; 581 Drazen, J.C., 215; 224 Drever, M.C., 522, 523, 525; 583; See Hobson, K.A., 589; See Taylor, R.H., 604 Drew, J.D. See Waithman, J.D., 606 Driessen, M.M., 573; 583; See Mallick, S.A., 594 Drinkwater, K.F., 243, 244, 270, 277, 282; 296 Driscoll, C.T., 243; 296 Dromberg, P. See Stehn, A., 453; 488 Druart, J.-C. See Barker, P., 162 Du, C., 56; 120 Duarte, C.M., 255, 257, 259, 262; 296; 555; 583; See Bach, S.S., 294; See Marba, N., 257; 302; See Terrados, J., 255; 307; See Vermaat, J.E., 308 Ducet, N., 43, 52; 120 Duck, C.D. See Pomeroy, P.P., 599 Ducklow, H. See Oguz, T., 131 Ducklow, H.W. See Oguz, T., 131; See Robinson, A.R., 229 Dudgeon, S. See Vadas, R.L., 424 Duffy, L.K., 539, 568; 583 Duggins, D.O., 541; 583; See Eckman, J.E., 274; 296 Duggins, D.O. See Estes, J.A., 365, 400; 414; See Hammerstrom, K., 588 Duigan, P. See Barrett, T., 576 Duineveld, G.C.A., 175, 199, 216; 224; See Boon, A.R., 199, 219; 223; See de Wilde, P.A.W.J., 223; See Witbaard, R., 232

622

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Dukowicz, J.K. See Hunke, E.C., 81; 124; See Treguier, A.M., 139 Dunbar, R., 160; 162 Dunbar, R.B. See DiTullio, G.R., 224 Dunham, K.M., 572; 583 Dunn, E.L. See Benke, A.C., 294 Dunn, J.L. See Frasca, S., 586 Dunstone, N., 543; 583; See Birks, J.D.S., 521, 543, 544; 578; See Bonesi, L., 578 Dunton, K.H. See Lee, K.-S., 259; 301 Duran, L.R., 352, 397; 413 Durand, D. See Johannessen, O.M., 125 Durand, N., 59; 120 Dustan, P., 271; 296 Duwe, K.C. See Nöhren, I., 131 Dybern, B.I., 466; 480 Dyer, K.R., 73; 120; 236; 296; See Charnock, H., 116; See Howarth, M.J., 124 Dyke, P.P.G., 51; 120 Dymond, J. See Honjo, S., 225; See Walsh, I., 232 Dymov, V.I. See Rozhkov, V.A., 135 DYNAMO Group, 44, 51; 120 Dynesius, M. See Jansson, R., 299 Eanes, R.J., 24; 29; See Tapley, B.D., 34 Eason, C.T. See Ogilvie, S.C., 598 Ebbinge, B.S. See Spaans, B., 603 Ebenhard, T., 493, 494; 583 Ebenhöh, W. See Baretta, J.W., 113 Ebert, T.A., 365, 369, 370, 387, 395, 397, 403, 404, 405, 407; 414; See Dixon, J.D., 413; See Schroeter, S.C., 422 Ebling, F.J. See Muntz, L., 419 Echardour, A. See Sibuet, M., 230 Echevin, V., 104; 120 Eckman, J.E., 274; 296; 326; 336; See Jumars, P.A., 328; 337; See Thistle, D., 313, 329, 331; 341 Eckrich, C.E., 492, 565; 583 Edesa, S. See Levin, L.A., 328; 338 Edgar, G.J., 250; 296 Edler, L. See Rydberg, L., 487 Edlin, H.L., 505, 526, 556; 583 Edmunds, H. See Howarth, M.J., 124 Edwards, A. See Watts, L.J., 140 Edwards, A.C. See Domburg, P., 582 Edwards, C.W. See Dowler, R.C., 583 Edwards, J.M., 501; 583 Edwards, M., 506; 583 Edwards, N.R. See Killworth, P.D., 62; 126

Edwards, P.B. See Dayton, P.K., 413 Edyvane, K. See Jones, G.K., 299 EEC (European Economic Community), 566; 584 Eero, M. See Ojaveer, H., 485 Egbert, G. See Shum, C.K., 33 Egge, J.K. See Aksnes, D.L., 111 Eglinton, G. See Bruland, K.W., 223; See Conte, M.H., 223 Ehrenberg, C.G., 143, 161; 163 Ehrenstein, U., 56; 120 Ehrlich, P.R. See Postel, S.L., 304 Eicken, H., 144, 145, 148, 150, 153, 161; 163; See Krembs, C., 165; See Lange, M.A., 165; See Smetacek, V., 168 Eicken, H. See Thomas, D.N., 168 Eide, L.I. See Røed, L.P., 134 Eiﬂer, W., 45; 120 Einarsson, S., 392; 414; See Andrew N.L., 343– 425 Eischeid, I. See Kiehl, K., 592 Eisma, D., 249; 296 Ekebjærg, L.C. See Vested, H.J., 139 Eknes, M., 67; 120 El Bahri, L., 504; 584 Eldevik, T., 38; 120 Eldin, A.T. See Abouheif, M.A., 575 Eldridge, M.D.B., 495; 584 El Garawany, M.M. See Shaltout, K.H., 602 El Halawany, E.F. See Shaltout, K.H., 602 Eliassen, I.K., 38; 120 El Kady, H.F. See Shaltout, K.H., 602 Elliott, A.J., 67; 120; See Clarke, S., 92; 116; See Li, Z., 77; 128 Elliott, J.E. See Howald, G.R., 590 Elliott III, H.W. See Wehausen, J.D., 562; 607 Ellis, G.E. See Watson, J.C., 424 Ellis-Evans, J.C. See Reay, D.S., 167 Ellwood, J., 530; 584 Elmgren, R., 431, 432, 433, 436, 444, 453, 468, 472, 477; 480, 481; See Cederwall, H., 444, 455, 472; 480 Elmgren, R. See Jonge, V.N. de, 483; See Larsson, U., 484; See Rosenberg, R., 487 Elmhagen, B., 532; 584 Elmhirst, R., 538; 584 Elmquist, T., 513; 584 El-Naghy, M.A. See Moubahser, A.H., 597 Elner, R.W., 404; 414 Els, S.F. See Ascaray, C.M., 576 Elton, C.S., 523; 584

623

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Emeis, K.-C. See Leipe, T., 227; See Loefﬂer, A., 227; See Witt, G., 232 Emerson, C. See Grant, J., 225 Emerson, S.R. See Jumars, P.A., 226 Emery, K.O., 25; 29 Emmerich, S. See Bazely, D.R., 577 Emmons, L.H. See Voss, R.S., 572; 606 Empson, R.A., 523; 584 Enﬁeld, D.B., 16; 29 Engbrodt, R. See Thomas, D.N., 168 Engedahl, H., 46, 103; 120 Engel, A. See Krembs, C., 158; 165 Engel, M. See Pohlmann, T., 133 Engeman, R.M. See Ramsey, D.S.L., 515, 570; 600 Ennet, P., 44, 99; 120; See Tamsalu, R., 44, 99; 120 Enriquez, S. See Vermaat, J.E., 308 Entsu, S. See Dohi, H., Erdmann, M. See Koop, K., 300 Eriksson, A.-K. See Andersson, L., 478 Eriksson, B. See Lännergren, C., 436; 483 Eriksson, O. See Kiviniemi, K., 565, 571; 592 Eriksson, S.P., 476; 481 Eriksson Wiklund, A.-K. See Andersson, L., 478 Erkkilae, B. See Jutila, H.M., 504; 591 Erlinge, S. See Angerbjorn, A., 575 Erwin, R.M., 495, 535, 545; 584 Erwin, T.L., 318, 319; 336 Eschler, B.M., 573; 584 Eschler, B.M. See Lawler, I.R., 593 Esfandiari, J., 573; 584 Espedal, H. See Johannessen, O.M., 125 Espino, M., 46, 57; 120; See Maidana, A.M., 129 Espos, C See Castilla, J.C., 412 Esselink, P., 502; 584 Estes, J.A., 365, 400; 414; 493, 540, 541; 583 Estes, J.A. Duggins, D.O., 583; See Reidman, M.L., 400; 421; See Simenstad, C.A., 422; See VanBlaricom, G.R., 400; 424; See Watt, J., 606 Estournel, C., 49, 86; 120; See Auclair, F., 112; See Johns, B., 125; See Marsaleix, P., 129; See Pinazo, C., 133 Etter, R.J., 315, 327; 336; See Chase, M.R., 335; See Levin, L.A., 342; See Rex, M.A., 327; 339; See Watts, M.C., 342 Evans, C.L., 472; 481 Evans, F., 511, 583 Evans, S. See Elmgren, R., 481

Evensen, G., 46, 103; 120; See Eknes, M., 67; 120; See Johannessen, O.M., 125; See Echevin, V., 120 Evers K.-U. See Eicken, H., 163 Eversberg, U. See Czytrich, H., 223 Ewins, P.J., 540; 583 Eydal, M. See Skirnisson, K., 603 Ezat, U. See Gehlen, M., 224 Ezer, T., 16, 22; 29; See Chassignet, E.P., 116; See Mellor, G.L., 54; 130 Fabiano, F. See Guglielmo, L., 164 Fabiano, M. See Misic, C., 166; See Pusceddu, A., 167 Fabiszisky, B. See Dick, S., 119 Fagen, J.M. See Fagen, R., 546; 585 Fagen, R., 546; 584 Fahl, K., 159; 163 Fahrbach, E. See Smetacek, V., 168 Fain, S.R. See Kretzmann, M.B., 592 Fairbridge, R.W., 247; 297 Fairley, J. See Tangney, D., 604 Falandysz, J., 444; 481 Falconer, R.A. See Chen, Y., 71; 116; See Lin, B., 44, 59, 63, 85; 128; See Wu, Y., 59; 140 Falkowski, P. Se Sancetta, C., 230 Falkowski, P.G., 333; 336; See Behrenfeld, M.J., 333; 335 Fallick, A.E. See Mitchell, L., 228 Fancy, S.G. See Walsh, N.E., 606 Fanjul, E.A. See Albiach, J.C.C., 111; See Ozer, J., 132 FAO, 346, 347; 414 Farber, S. See Costanza, R., 295 Farbrot, T. See Kristiansen, S., 165 Faro, J.B. See Ben-David, M., 577; See Bowyer, R.T., 578; See Duffy, L.K., 583 Farrar, P. See Westerink, J.J., 140 Farrington, J.W. See Lee, C., 227 Fasham, M.J.R. See Robinson, A.R., 229 Fastenau, H.C. See Rogers-Bennett, L., 421 Fauchald, K. See Thistle, D., 341 Fauquier, D.A. See Goldstein, T., 587 Faye, B., 513; 584 Feare, C.J., 492; 585 Fedak, M.A., 549; 585 Fegley, J. See Vadas, R.L., 423 Feldmann, J., 557; 585 Fellesen, G., 461, 462, 468; 481 Fennel, K., 97; 121

624

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Fennel, W., 55, 82, 97; 121; See Schmidt, M., 135 Fenoglio, L., 26; 29 Ferguson, S.H., 514, 546, 572; 585 Fernand, L. See Hill, A.E., 123 Fernandes, M.E.B., 512, 513, 535, 563, 564; 585 Fernández, V. See Pinot, J.-M., 133 Fernández-Llario, P., 560; 585 Ferreira, S.M., 507; 585; See van Aarde, R.J., 605 Ferreras, P., 543; 585; See Gaona, P., 586 Ferrero, T. See Lambshead, P.J.D., 338 Ferrero, T.J. See Lambshead, P.J.D., 338 Ferrier, R.C. See Domburg, P., 582 Ferron, L. See Dewailly, E., 582 Feunteun, E. See Laffaille, P., 593 Février, S., 45, 47, 51, 60, 90; 121 Fiandrino, A. See Durand, N., 120 Field, K. See Giovannoni, S., 336 Field, R. See Popay, I., 504; 599 Fielden, L.J., 517; 585 Fieux, M. See Molcard, R., 32 Filarski, J., 448; 481 Filip, L. See Bazely, D.R., 577 Fillaux, J. See Pinturier-Geiss, L., 229 Filloux, J.H., 20; 29; See Chave, A.D., 28; See Luther, D.S., 31 Finlay, B.J. See Palmer, M.A., 339 Finlayson, B.L., 238, 239; 297 Finnie, J.K. See Pain, D.J., 598 Finucane, J.H. See Grimes, C.B., 247, 271, 278, 285; 298 Fischer, G., 145, 160; 163; See Mackensen, A., 227; See Wefer, G., 145; 169 Fischer, K. See Walsh, I., 232 Fish, J.D., 493; 585 Fish, S. See Fish, J.D., 493; 585 Fisher, E.C. See Smith Jr, K.L., 231 Fisher, H.D. See Stenson, G.B., 603 Fitt, W.K., 272; 297 Fitzgerald, B.M., 494, 536; 585 Fitzgibbon, C.D., 571; 585 Fitznar H.P. See Amon R.M.W., 162 Fitzsimmons, K. See Galindo-Bect, M.S., 297 Flament, P. See Yoder, J.A., 232 Flather, R. See Monbaliu, J., 130 Flather, R.A., 27; 29; 38, 43, 44, 46, 51, 56, 80, 84, 100, 105, 106, 107, 108; 121; See Andersen, O.B., 28; See Davies, A.M., 118; See Mason, D.C., 129; See Smith, J.A., 137;

See Smithson, M.J., 33; See Tsimplis, M.N., 34; 139 Flatt, D. See Prandle, D., 134 Flegg, J.J.M., 522; 585 Fleischer, S. See Rosenberg, R., 487 Fleming, T.H. See Ceballos, G., 580; See Wilkinson, G.S., 518; 607 Flemming, N.C. See Prandle, D., 37; 134 Flessa, K.W. See Kowalewski, M., 300; See Rodriguez, C.A., 305 Flindt, M.R. See Martins, I., 302 Flint, P.L. See Anthony R.M., 575 Floderus, S. See Rydberg, L., 487 Florian, J.D. See Dixon, K.R., 254; 296 Floury, L. See Sibuet, M., 230 Fluharty, D., 408; 414 Flux, J.E.C., 494, 521; 585 Flynn, K.J., 154, 155; 163 Focardi, S. See Leonzio, C., 593; See Marsili, L., 498; 595 Foden, P.R. See Spencer, R., 19; 34 Fogden, S.C.L., 492, 549, 551; 585 Fohrmann, H., 92; 121; See Jungclaus, J.H., 125; See Kämpf, J., 63, 93; 126 Fojt, E., 509; 585 Foldvik, A. See Furevik, T., 97; 121 Foley, W.J. See Eschler, B.M., 584; See Lawler, I.R., 593 Folgarait, P. See Brussaard, L., 335 Fong, P., 256, 264, 265, 266; 297 Fonseca, M.S., 555; 585; See Kenworthy, W.J., 299 Fonseca, T. See Clément, A., 412 Fonselius, S.H., 428, 430, 446, 448, 469; 481 Fontes, J.-C. See Barker, P., 162 Forbes, R.M. See Bell, M.J., 113 Forcada, J., 459; 585 Ford, J. See Salter, E., 537; 601 Ford, J.K.B. See Watson, J.C., 424 Fordham, R.A. See Sutherland, R.D., 604 Foreman, K. See Valiela, I., 307 Forget, P. See Durand, N., 120; See Guan, C., 122 Förlin, L. See Rosenberg, R., 487; See Smith, S., 488 Forman, S.L., 569; 586 Fornes, W. See Levin, L., 227 Fornes, W.L., 219; 224; See Miller, R.J., 228 Fortes, M.D. See Bach, S.S., 294; See Duarte, C.M., 296; See Terrados, J., 307; See Vermaat, J.E., 308

625

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Fortunato, A.B., 43, 45, 47, 58, 65, 67, 84; 121 Forward Jr, R.B., 279; 297 Forys, E.A., 470, 571; 586 Fossi, C. See Leonzio, C., 593 Foster, G.N. See McCracken, D.I., 511; 595 Foster, M.S., 400; 414; See Van Wagenen, R.F., 606 Fothergill, A., 549, 553; 586 Fourqurean, J.W. See Powell, G.V.N., 304 Fowler, D. See Sutton, M.A., 604 Fowler, S.W., 172, 192; 224 Fox, A.D., 60; 121; See Green, A.J., 298 Fox, B.J. See Haering, R., 522; 588 Føyn, L. See Laane, R.W.P.M., 126 Fradon, B., 100; 121 Frafjord, K., 531, 532; 586 Fragoso, C. See Brussaard, L., 335 Frame, J., 506; 586 France, S.C., 318; 336 Francis, J.M., 552; 586 Francis, O. See Shum, C.K., 33 Francois, R. See Honjo, S., 225 Frank, K.T. See Drinkwater, K.F., 243, 244; 296 Franke, U. See Jorgensen, B.B., 226 Frankignoul, C. See Février, S., 121 Frasca, S., 528; 586 Fraser, G.C. See Sammut, J., 305 Fraser, R.H., 323; 336 Fraser, W.R. See Ribic, C.A., 601 Fraunie, P. See Durand, N., 120; See VerdierBonnet, C., 139; See Arnoux-Chiavassa, S., 112 Freckman, D.W. See Brussaard, L., 335 Frederiksen, J.H. See Deigaard, R., 118 Fredsoe, J. See O’Connor, B.A., 131 Freitag, J. See Eicken, H., 163 French, F.E. See Konai, M., 592 French, J.R. See Möller, I., 130 Frewtrell, L. See Wyer, M., 608 Frey, R.W. See Edwards, J.M., 583 Frezzotti, M. See Raper, S.C.B., 33 Frid, A., 562; 586 Friedrichs, C.T. See Ip, J.T.C., 124 Frithsen, J.B. See Widbom, B., 214, 215; 232 Fritsen, C.H., 147, 152, 153; 163; See Arrigo, K.R., 162 Fritz, S. See Jacoby, C., 590 Fritzsche, D., 472, 474; 481 Froelich, P.N. See Rau, G.H., 167 Froend, R. See Davis, J.A., 240; 296 Froget, C. See Leveau, M., 306

Frohse, A. See Becker, G.A., 113 Frutiger, A. See Matthaei, C.D., 302 Fry, D. See Rice, A.L., 339 Fry, J. See Rice, A.L., 229 Fu, L.-L., 2, 3, 4; 29; See Chelton, D.B., 29; See Fukumori, I., 29; See Mitchum, G.T., 32 Fuglei, E. See Lonne, I., 528; 594 Fuhrman, J.A., 322; 336 Fujiwara, S. See Kitazato, H., 226 Fukumori, I., 16; 29 Fuller, R.M., 509; 586 Funicelli, N.A., 252; 297 Funkquist, L., 45, 105, 108; 121 Furbish, C.E., 503, 556, 573; 586 Furevik, T., 97; 121 Furgal, C.M., 547; 586 Furnari, G., See Cormaci, M., 401; 413 Furness, R.W., 494, 523, 530, 536, 559, 561; 586; See Monteiro, L.R., 596; See Ross, B.P., 497; 601 Futterer, D.K. See Mackensen, A., 227 Fütterer, D. See Fischer, G., 163 Fyns Amt, 456; 481 Gabrielsen, G.W. See Skaare, J.U., 603 Gade, M. See Metzner, M., 130 Gage, J.D., 215; 224; 311, 315, 316, 320, 324; 336; See Campos-Creasey, L.S., 223; See Levin, L.A., 315, 326; 338; See Mitchell, L., 228 Gage, K.S. See McPhaden, M.J., 31 Gage, L.J. See Gerber, J.A., 587 Gagosian, R.B. See Lee, C., 227 Gaidos, E.J., 144; 163 Gaines, S.D. See Botsford, L.W., 411 Galat, G., 564; 586 Galat-Luong, A. See Galat, G., 564; 586 Galaviz, J.L.R. See Yanez-Arancibia, A., 608 Galdikas, B.M.F., 564; 586 Galera, J. See Lozano, J., 418 Gales, N.J., 527, 528; 586 Galina-Tessaro, P. See Alvarez-Cardenas, S., 575 Galindo-Bect, L.A. See Galindo-Bect, M.S., 297 Galindo-Bect, M.S., 245; 297 Gallagher, E.D. See Jumars, P.A., 325; 337 Galperin, B., 75; 121; See Livingston, R.J., 301 Gammelsrod, T., 276; 297 Ganachaud, A. See Pinot, J.-M., 133 Gaona, P., 572, 576; 586 Garcia, S., 376, 403; 414

626

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García, E. See Tintoré, J., 138 García, M.A. See Espino, M., 120; See Maidana, A.M., 129 Garcia Rubies, A. See Ballesteros, E., 384; 410 Garcia-Hernandez, J. See Galindo-Bect, M.S., 297 Garcia-Ladona, E. See Varela, R.A., 139 Garda, H. See Nichols, D.S., 166 Gardner, I.A. See Sweitzer, R.A., 604; See Waithman, J.D., 606 Gardner, J.L., 572; 586 Gardner, J.V. See Harris, P.T., 298 Gardner, L.R., 570; 586 Gardner, W.D., 183, 190, 205; 224; See Rowe, G.T., 323; 340 Garnier, F. See Brunet, S., 115 Garnier, J.M. See Pham, M.K., 133 Garrabou, J. See Sala, E., 421 Garreau, P. See Hoch, T., 78, 98; 123 Garrett, C. See Ross, T., 33 Garrison, D.L., 142, 144, 152, 153, 156; 163; See Buck, K.R., 162; See DiTullio, G., 162; See Palmisano, A.C., 144, 156; 166 Garritt, R.H. See Peterson, B.J., 228 Garshelis, D.L., 541; 586 Garshelis, J.A. See Garshelis, D.L., 586 Garside, C. See Malone, T.C., 228 Garvine, R.W., 249; 297 Gaspar, P., 22; 29; See Ponte, R.M., 22; 23 Gasse, F. See Barker, P., 162 Gasseling, A.P. See van Katwijk, M.M., 308 Gassett, J.W., 567; 586 Gaston, A.J. See Gilchrist, H.G., 526; 587 Gauzelin, P. See Ducet, N., 120 Gava, A., 504; 587 Gazo, M., 510; 587 Gebauer, P., 349, 406; 414 Geertz-Hansen, O. See Terrados, J., 307 Geffen, E. See Kutiel, P., 593 Gehlen, M., 172; 224 Gehrke, P.C. See Harris, J.H., 281; 298 Gekeler, J., 104; 121; See Berlamont, J., 114 Gelin, F. See Volkmann, J.K., 169 Gellers-Barkmann, S. See Tappin, A.D., 138 Genco, M.L. See Le Provost, C., 31; 127, 128 Genov, P.V. See Massei, G., 595 Gent, P.R., 73, 89; 121 Gentien, P. See Wolanski, E., 140 George, K.J., 49, 72, 84; 121 George, K.H. See Günther, S., 164 George, M. See Dick, S., 119

Georges, J.Y., 551; 587 Gerber, J. See Beckmen, K.B., 577 Gerber, J.A., 552, 553; 587 Gerber, L.R., 407; 414 Gerdes, D. See Thomas, D.N., 168 Gerdes, R. See Grifﬁes, S.M., 122 Gerkema, T., 102; 121 Gerlach, S.A. See Graf, G., 225 Gerland, S. See Haas, C., 164 Gerring, P.K. See Andrew, N.L., 343– 425; See McShane, P.E., 419 Gerritsen, H., 44, 47, 59, 92, 93; 121, 122; See Davies, A.M., 68; 117; See Monbaliu, J., 130; See Vos, R.J., 139 Gerrodette, T., 492; 587 Gersonde, R. See Fischer, G., 163; See Zielinski, U., 156; 169 Gettner, S. See Kiehl, K., 592 Ghil, M. See Chassignet, E.P., 116; See Karaca, M., 126; See Unal, Y.S., 21; 34 Giannelli, V. See Haas, C., 164; See Thomas, D.N., 168 Gibbard, P.L. See Bates, M.R., 577 Gibbs, P. See Harris, P.T., 298 Gibert, J. See Palmer, M.A., 339 Gibson, J.A., 522; 587 Gibson, J.A.E., 160, 163 Gibson, R.N., 492, 539; 587 Gibson-Hill, C.A., 522; 587 Giese, A.C., 396; 414; See Holland, L.Z., 415 Giese, H. See Becker, G.A., 113 Giesy, J.P. See Watanabe, M., 606 Gilbert, C.L. See Gildersleeve, R.R., 587 Gilbert, L. See Long, D.J., 551; 594 Gilchrist, H.G., 526; 587 Gildersleeve, R.R., 504, 567; 587 Gilg, O. See Sittler, B., 602 Gill, H.S., 282, 283; 297 Gill, R.M.A., 572; 587 Gillanders, B.M., 233–309; 253; 297; See Kingsford, M.J., 300 Gille, S.T., 19; 29, 30 Gilmartin, W.G. See De Long, R.L., 582; See Gerrodette, T., 492; 587; See Hiruki, L.M., 589; See Kretzmann, M.B., 592 Gilmour, J., 269; 297 Gilpin, M.E. See Bender, E.A., 294 Giménez-Curto, L.A., 78; 122 Gingras, S. See Dewailly, E., 582 Giovannoni, S., 322; 336

627

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Giribet, G. See Palacín, C., 420; See Turon, X., 423 Gjertz, I., 548; 587 Gjevik, B. See Røed, L.P., 134 Glaister, J.P., 277; 297 Glasby, T.M., 244; 297 Glaser, B. See Bennett, B.A., 335 Glasgow, H.B., 243; 297 Glatts, R.C. See Baldwin, R.J., 222; See Smith Jr, K.L., 230, 231 Gleitz, M., 149, 150, 151, 152, 153, 156, 158, 159, 160; 163, 164; 185, 205; 224; See Günther, S., 164; See Thomas, D.N., 159; 168 Glenn, E.P. See Galindo-Bect, M.S., 297; See Swingle, R.S., 604 Glibert, P.M. See Lomas, M.W., 154, 155; 165 Glorioso, P.D., 101; 122 Glover, A. See Lambshead, P.J.D., 338 Glud, R.N. See Kühl, M., 165; See Rysgaard, S., 167 Goater, C.P. See Goss-Custard, J.D., 587 Godinez, V.M. See Lavin, M.F., 301 Godoy, C. See Bustos, E., 412; See Moreno, C.A., 419 Goedkoop, W., 173, 195, 214; 224 Goff, M.L. See Davis, J.B., 582 Goffart, A. See Hoffmann, L., 415 Goksoyr, A. See Skaare, J.U., 603 Goldstein, T., 527; 587 Goldsworthy, S. See Robinson, S., 601 Golubkov, S.M. See Telesh, I.V., 488 Gomersall, C.H. See Bullock, I.D., 509; 579 Gomez, B.P. See Albiach, J.C.C., 111 Gomez, E.D. See Juinio, M.A., 416 Gomez-Gesteira, M., 47, 73; 122; See Taboada, J.J., 138 Gons, H.J. See Otten, J.H., 228 González, L., 406; 415 Gonzalez, L.M. See Gazo, M., 587 González, S. See Stotz, W., 423 González-Solís, J., 551; 587 Good, E.I., 177, 200; 224 Gooday, A.J., 173, 175, 177, 192, 195, 211, 214, 216; 224, 225; 316, 326, 329, 331; 336; See Lambshead, P.J.D., 329; 338; See Levin, L.A., 342; See Thiel, H., 231, 341 Goodfriend, G.A. See Kowalewski, M., 300 Goodwin, V. See Wyer, M.D., 608 Göransson, P., 462; 481 Gordon, A.L. See Susanto, R.D., 34 Gordon, D.M., 262, 267; 297, 298

Gordon, L. See Rau, G.H., 167 Gordon, L.I. See Garrison, D.L., 163; See Smetacek, V., 168 Gorman, M.L. See Corp, N., 581; See Massei, G., 595; See Zubaid, A., 525; 608 Gorman, R.M., 100; 122 Gornitz, V., 16, 27; 30 Gorny, V.I., 91; 122 Gosink, J.J. See Staley, J.T., 144, 159; 168 Gosper, D.G., 286; 298 Goss, R.A. See Skinner, J.D., 603 Goss-Custard, J.D., 530, 545; 587 Gosselck, F. See Andersin, A.-B., 478 Gosselin, M. See Arrigo, K.R., 162 Gosset, D. See Bourel, B., 578 Goszczynski, J., 566; 587 Gotschalk, C. See Alldredge, A.L., 222 Gotschalk, C.C. See Macintyre, S., 227 Gough, C. See Thomas, D.N., 168 Gould, J. See Siedler, G., 33 Goulter, I.C. See Karim, K., 299 Goutx, M. See Leveau, M., 301 Govoni, J.J., 247, 285; 298 Gowing, M. See Wishner, K., 232 Graber, H.C. See Signell, R.P., 136 Graddon, D.J. See Edgar, G.J., 296 Gradinger, R. See Eicken, H., 163; See Krembs, C., 165; See Mock, T., 159; 166; See Schnack-Schiel, S.B., 168; See Weissenberger, J., 169 Graf, G., 172, 179, 181, 187, 190, 194, 200, 203, 206, 207, 209, 211, 212, 213, 214, 218; 225; See Czytrich, H., 223; See Linke, P., 227 Gran, V., 439, 444; 481; See Leppänen, J.-M., 484; See Pitkänen, H., 486 Granéli, W. See Rydberg, L., 487 Grange, K.R., 274; 298; See Witman, J.D., 274; 308 Granja, H.M., 507; 587 Grant, A., 324; 337; See Young, E.F., 141 Grant, J., 211; 225; See Amos, C.L., 222 Grant, R. See Kronvang, B., 300 Grassle, J.F., 179; 225; 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 327, 328, 330, 331, 332; 337; See Blake, J.A., 313; 335; See Etter, R.J., 315, 327; 336; See Ray, G.C., 318; 339; See Smith, W., 314; 340; See Snelgrove, P.V.R., 341; See Grassle, J.F., 331; 337 Grasso, M. See Costanza, R., 295 Gray, A.J., 501, 521; 587

628

AUTHOR I ND E X

Gray, C.A. See Kingsford, M.J., 285, 286; 300 Gray, J. See Mirza, F.B., 467; 484 Gray, J.S., 314, 319, 320, 321; 337; See Rosenberg, R., 487 Greatbatch, R.J. See Ezer, T., 29; See Sheng, J., 136 Grebmeier, J.M., 205; 225; See DiTullio, G.R., 224 Green, A.J., 235; 298 Green, C.T., 212; 225 Green, J., 525, 535, 545; 588; See Green, R., 538; 588 Green, M.O., 83; 122; See Jago, C.F., 226 Green, R., 538; 588 Green, R.E., 516; 588; See Jackson, D.B., 516; 590 Greenhill, A.R. See Nichols, D.S., 166 Greenwald, G.M., 271, 273, 292; 298 Greenwood, J.E., 156; 164 Greenwood, Y., 559; 588; See Orpin, C.G., 598 Grégoire, M., 98; 122 Grégoire, M. See Delhez, E.J.M., 119 Gregory, J.M. See Church, J.A., 29 Gren, I.-M., 477; 481 Gresham, C. See Gardner, L.R., 586 Gretz, M.R. See Hoagland, K.D., 164 Greve, W., 54, 107; 122 Grey, K.A. See Gordon, D.M., 298 Grieshaber, M. See Schöttler, U., 472, 475; 487 Grifﬁes, S.M., 42, 51, 59, 62, 73; 122 Grifﬁth, B. See Wolfe, S.A., 607 Grifﬁths, C.L. See Tarr, J.G., 604 Grillas, P. See Mesleard, F., 596 Grimaldo, N.S. See Bahamón, N., 113 Grimes, C.B., 234, 236, 237, 246, 247, 249, 250, 252, 271, 278, 285; 298; See Govoni, J.J., 285; 298; See Travis, J., 423 Grimshaw, R., 53; 122 Grimvall, A. See Laznik, M., 482; See Stålnacke, P., 488 Grip, K. See Rosenberg, R., 487 Gripenberg, S., 445; 481 Groeneveld, G. See Laane, R.W.P.M., 126 Gromisz, S. See Renk, H., 486 Gross, T.F. See Levin, L.A., 338 Grossmann, S., 147, 148, 153, 154, 156; 164; See Gleitz, M., 163 Groten, E. See Fenoglio, L., 26; 29 Guan, C., 99; 122 Gubbels, M.E. See Karim, K., 299 Gudger, E.W., 519; 588

Guegueniat, P. See Salomon, J.C., 135 Guglielmo, L., 153, 157; 164 Guidi-Guilvard, L.D. See Gehlen, M., 224 Guilette Jr, L.J. See Crain, D.A., 581 Guillaud, J.-F., 97; 122 Guillou, M., 390, 405; 415 Guinet, C., 528; 588; See Georges, J.Y., 551; 587 Guisado, C., 406; 415; See Bückle, F., 412; See González, L., 415 Guisado, C.B., 353; 415 Guizen, K., 52; 122 Gulland, F. See Scholin, C.A., 602 Gulland, F.M.D., 553; 588; See Dailey, M.D., 582; See Goldstein, T., 587 Gulland, J.A., 365, 376, 402; 415 Gulliksen, B. See Haug, T., 527; 589; See Horner, R., 164; See Legendre, L., 165 Gunn, A. See Miller, F.L., 596 Gunnarsson, E. See Skirnisson, K., 603 Gunter, G., 249, 282, 283; 298 Günther, H. See Lane, A., 127; See Monbaliu, J., 130; See Schneggenburger, C., 135, 136 Günther, S., 147, 149, 152, 153, 154, 156, 158, 161; 164 Guo, P. See Lu, N., 301 Gupta, V.V.S.R. See Brussaard, L., 335 Gurney, C. See Mason, D.C., 129 Gustaffson, B.G., 45, 59, 67; 122 Gustafson, D.E., See Stoecker, D.K., 168 Gustafsson, N., 43; 122 Gustavsson, B. See Westman, P., 489 Gutiérrez, J., 353; 415 Gutt, J., 185, 188, 205; 225 Gwilliam, C.S. See Webb, D.J., 140 Haake, B. See Rixen, T., 229 Haaker, P. See Karpov, K.A., 416 Haapala, J., 82; 122 Haardt, H. See Peinert, R., 228 Haas, C., 147, 152, 161; 164; See Herborg, L.-M., 164; See Kennedy, H., 165; See Mock, T., 165; See Schnack-Schiel, S.B., 166; See Thomas, D.N., 168 Hackett, B., 64; 122; See Røed, L.P., 134 Hackett, K.J. See Konai, M., 592 Hackney, C.T., 505, 588 Haddock, S.H.D. See Alldredge, A.L., 222 Haedrich, R.L. See Carney, R.S., 335; See Rowe, G.T., 340 Haelters, J. See Jauniaux, T., 591

629

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Haering, R., 522; 588 Haesloop, U. See Schuchardt, B., 305 Hagen, N., 344, 346, 393, 395; 415 Hagerman, L., 472, 473, 474; 481; See Sandberg-Kilpi, E., 487 Hagmeier, A., 448, 450; 481 Hagy, J.D. See Boynton, W.R., 294 Hahlbeck, E., 472; 482 Haidvogel, D. See Song, Y., 64; 137 Haidvogel, D.B., 42, 44, 45, 48, 51, 59, 70, 105; 122, 123; See Chassignet, E.P., 116; See Curchitser, E.N., 117 Hailey, A., 522; 588 Haines, B.J., 18; 30; See Chelton, D.B., 29 Haines, K., 73, 83, 89; 123; See Drakopoulos, P.G., 120; See Myers, P.G., 50; 131; See Samuel, S., 135; See Wu, P., 89; 140 Hajas, W. See Campbell, A., 412 Håkansson, E. See Andersson, L., 478 Häkkilä, S. See Mölsä, H., 484 Haley, D., Hall, A. See Barrett, T., 576 Hall, C.A.S. See Day Jr, J.W., 582 Hall, F.J. See Greenwood, Y., 588; See Orpin, C.G., 598 Hall, G.P. See Eldridge, M.D.B., 584 Hall, K. See Keesing, J., 346, 374; 416 Hall, P. See Davies, A.M., 76, 77; 117, 118; See Tengberg, A., 231 Hall, R. See Eicken, H., 163 Hall, S.J.G., 492, 559, 560; 588 Hall, W.K. See Brown, W.K., 579 Halpern, D. See McPhaden, M.J., 31 Halverson, G.P. See Hoffman, P.F., 164 Hamm, L. See Péchon, P., 132 Hammerstrom, K., 573; 588; See Kenworthy, W.J., 299 Hammill, M. See Kovacs, K., 592 Hammill, M.O., 546, 547, 572; 588 Hammond, D. See Smith, C.R., 230 Hammond, P.S. See Forcada, J., 585 Hampson, G.R. See Sanders, H.L., 340 Hamre, T. See Johannessen, O.M., 125 Hand, K.P. See Chyba, C.F., 144; 162 Handley, L.L. See Raven, J.A., 167 Hannan, J.C. See Pollard, D.A., 254, 255; 304 Hanni, K. See Beckmen, K.B., 577 Hanni, K.D., 498, 549; 588; See Goldstein, T., 587 Hänninen, J., 438; 482 Hannon, B. See Costanza, R., 295

Hansell, D.A. See Carlson, C.A., 156, 157; 162 Hansen, A.M., 92; 123 Hansen, G.B. See McCord, T.B., 166 Hansen, J.L.S. See Kiørboe, T., 226 Hansen, L.S., 208; 225 Hanson, P.J., 253; 298 Hapter, R. See Semovski, S.V., 136 Haque, M.Z. See Roy, G.D., 135 Harangozo, S.A., 15; 30; See Spencer, R., 34; See Vassie, J.M., 34 Harbo, R., 369; 415 Harbo, R.M. See Campbell, A., 363; 412; See Sloan, N.A., 422 Harcourt, A.H., 495; 588 Harcourt, R., 552; 588 Hardcastle, P.J. See Williams, J.J., 232 Harder, M., 82; 123; See Hilmer, M., 123; See Steiner, N., 138 Harding, L.W., 270; 298 Hardy, A., 508; 588 Hardy, T.A. See Bode, L., 38, 51; 114 Harestad, A.S. See Drever, M.C., 522, 523; 583 Hargrave, B.T., 187, 202; 225; See Jumars, P.A., 226 Hargraves, P.E. See Riaux-Gobin, C., 229 Hargreaves, J.C. See Annan, J.D., 104; 112; See Monbaliu, J., 130 Harjadi, P. See Tsuji, Y., 605 Harlow, C., 531; 588 Harmelin-Vivien, M. See Sala, E., 421 Harms, I.H., 73, 82, 86; 123 Harper, R.J., 539; 588 Harrington, F.H., 566; 588 Harris, C.J., 538, 540; 588 Harris, D., 506; 589 Harris, J. See Proctor, R., 134 Harris, J.E. See Enﬁeld, D.B., 16; 29 Harris, J.H., 280, 281; 298 Harris, J.R.W., 44, 97; 123; See Liu, Y.P., 128 Harris, L.G., 405; 415; See Balch, T., 410 Harris, P.T., 250; 298 Harris, S. See Walsh, A.L., 571; 606 Harrison, A.J. See Prandle, D., 134; See Spencer, R., 34; See Vassie, J.M., 34 Harrison, J. See Imber, M., 590 Harrison, M. See Imber, M., 590 Harrison, P. See Koop, K., 300 Harrison, P.J. See Thompson, P.A., 168 Harrison, R.J., 553, 555; 589 Harrison Matthews, L., 516, 551; 589 Harvey, J. See Scholin, C.A., 602

630

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Harvey, S.M. See Mitchell, L., 228 Harwell, M.A. See Fong, P., 256; 297 Harwell, M.C. See Fong, P., 297 Harwood, J. See Fedak, M.A., 585 Hasse, L. See Luthardt, H., 79; 128 Hasselmann, K. See Komen, G.J., 126 Hasselmann, S. See Komen, G.J., 126 Hasselrot, T.B., 445; 482 Hassid, S. See Galperin, B., 121 Hastings, A. See Botsford, L.W., 411 Hastrup, A. See Madsen, J., 594 Hasumi, H. See Grifﬁes, S.M., 122 Haswell-Smith, H., 522, 523; 589 Hatcher, B.G. See Scheibling, R.E., 347, 379, 381, 399; 422 Hattori, T. See Brussaard, L., 335 Haug, T., 527; 589 Haulena, M. See Scholin, C.A., 602 Hauser, D. See Fradon, B., 121 Hautlala, S. See Bray, N.A., 28 Haux, C. See Hylland, K., 567; 590 Hawell, J. See Green, R.E., 588 Hawkins, L.E. See Lambshead, P.J.D., 338 Hawksworth, D.L. See Brussaard, L., 335 Hay, C.H. See Villouta, E., 424 Hay, M.E. See Bolser, R.C., 573; 578; See Cronin, G., 266; 296 Hay, M.E. See Miller, M.W., 303 Haya, D., 384; 415 Hays, W.S.T., 516, 525, 526; 589 Haywood, M.D.E. See Vance, D.J., 308 Hazebroek, H., 519, 542, 560, 564; 589 Headland, R., 508, 522, 549, 551, 562; 589 Heales, D.S. See Vance, D.J., 308 Healy, R.W., 44; 123 Heaps, N.S., 65; 123 Hearn, C.J., 86; 123 Hearne, J.W. See Quinn, N.W., 304; See Wortmann, J., 309 Hebert, K. See Andrew N.L., 343– 425 Hecht, M.W., 43, 44, 46, 48, 51, 71, 72; 123 Heck, K.L., 268; 298 Hecker, B., 179, 189, 205; 225; 328; 337; See Gooday, A.J., 224 Hedges, S.B., 497; 589 Hedouin, V. See Bourel, B., 578 Heeger, T. See Gooday, A.J., 224; See Linke, P., 227 Heggberget, T.M., 540; 589 Heggelund, Y., 38; 123 Heidt, L.E. See Sturges, W.T., 168

Heinloo, J. See Vösumaa, U., 77; 140 Heinsohn, G., 554; 589 Heinsohn, G.E., 513; 589 Heip, C. See Smith, C.R., 340; See Snelgrove, P.V.R., 340 Heip, C.H. See Moodley, L., 228 Heiskanen, A.-S., 195; 225; See Leppänen, J.-M., 484 Heithaus, M.R. See Connor, R.C., 580 HELCOM, 428, 432, 433, 434, 439, 442, 443, 444, 446, 448, 449, 450, 451, 473; 482 Helland, A. See Berge, J.A., 479 Hellman, B. See Rosenberg, R., 468; 487 Helminen, O., 435; 482 Helmke, E., 144; 164 Hemleben, C. See Thiel, H., 231, 341 Hemminga, M.A. See Kamermans, P., 299 Hendrix, J.M. See Kalvass, P., 365, 366, 367, 368, 369, 370, 404; 416 Henegar, R.B. See Konai, M., 592 Heniche, M., 48, 84; 123 Hennigar, A.W. See Scheibling, R.E., 381, 382; 422 Hennings, I. See Metzner, M., 130 Henrichs, S.M., 210, 218; 225 Henriksen, E. See Skaare, J.U., 603 Henriksson, R., 457; 482 Hentschke, U. See Dick, S., 119; See Puls, W., 134 Herbaut, C. See Tusseau-Vuillemin, M.-H., 139 Herbert, R. See Castel, J., 295 Herbes, D. See Lane, A., 127 Herborg, L.-M., 156, 157, 159; 164 Hereu, B. See Sala, E., 421 Herfst, M.S., 538; 589 Herlihy, F. See Forman, S.L., 586 Herman, P.M. See Moodley, L., 228 Hernandez-Ayon, J.M. See Galindo-Bect, M.S., 297 Hernández-García, E. See Álvarez, A., 111 Hersbach, H. See Johnson, H.K., 125 Hersh, D. See Valiela, I., 307 Hersteinsson, P., 531, 532; 589; See Angerbjorn, A., 575; See Skirnisson, K., 603 Herunadi, B. See Susanto, R.D., 34 Hesp, P. See Hodgkin, E.P., 250; 298 Hesse, K.-J. See Dick, S., 119; See Pohlmann, T., 133 Hessler, R.R., 313, 316, 320, 326; 337; See Dayton, P.K., 323; 336; See Jumars, P.A., 316; 337; See Levin, L.A., 342; See Rex,

631

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M.A., 339; See Sanders, H.L., 313; 340; See Smith, C.R., 324; 340 Hewer, H.R., 510; 589 Hewson, R. See Kruuk, H., 538; 592 Heywood, K.J. See Gille, S.T., 30; See Hughes, C.W., 30; See Meredith, M.P., 31; See Rubython, K.E., 33 Hibbitts, C.A. See McCord, T.B., 166 Hibler, W.D., 81, 82, 86; 123 Hickling, G. See Coulson, J.C., 549, 553; 581 Hickling, G.J. See Ryan, C.J., 601 Hickman, G.C. See Fielden, L.J., 585 Hidalgo, C. See Zegers, J., 425 Hidalgo de Trucios, S.J. See Fernández-Llario, P., 585 Higashida, I. See Taki, J., 344, 396; 423 Higgins, R., 553; 589 Hilbig, B. See Thistle, D., 341 Hilborn, R., 292; 298; 370, 395, 402; 415 Hildebrand, M. See Martin-Jézéquel, V., 166 Hill, A.E., 91; 123 Hill, C. See Adcroft, A., 111; See Marshall, J., 129 Hill, J. See Beckmen, K.B., 577 Hill, M.R.J. See Smith, A.E., 547; 603 Hill, P.J. See Grange, K.R., 298 Hilmer, M., 85; 123; See Harder, M., 123 Hilton, G. See Furness, R.W., 586; See Green, A.J., 298 Himmelman, J.H., 393, 403; 415 Hines, A. See Bell, M.J., 113 Hines, A.H. See Pearse, J.S., 405; 420 Hinrichsen, H.-H. See Lehmann, A., 82, 90; 127 Hiroki, K. See Theede, H., 488 Hirst, A. See Grifﬁes, S.M., 122 Hiruki, L.M., 549, 551, 553; 589 Hoagland, K.D., 159; 164 Hoang, S., 104; 123 Hobbs, K. See Harbo, R.G., 369; 415; See Perry, R.I., 420 Hobson, K.A., 493, 522, 524; 589; See Drever, M.C., 583 Hoch, T., 78, 98, 123 Hockey, P.A.R., 493, 533; 589; See Leseberg, A., 593 Hocking, G.J. See Driessen, M.M., 583; See Mallick, S.A., 594 Hodgkin, E.P., 250; 298 Hoegh-Guldberg, O., 268, 269; 299; See King, C.K., 417; See Koop, K., 300 Hoelemann, J. See Haas, C., 164

Hoese, J.D., 529; 589 Hoffman, P.F., 144; 164 Hoffmann, L., 401; 415 Hogarth, P.J., 512, 513, 564; 589 Hoge, F.E. See Robinson, A.R., 229 Hohmann, S. See Raffaelli, D.G., 600 Højstrup, J. See Johnson, H.K., 125 Holland, D.M., 47, 64; 123 Holland, L.Z., 396; 415 Holland, W.R. See Hecht, M.W., 123 Holler, N.R., 570, 573; 589; See Oli, M.K., 589 Holling, C.S. See Walters, C.J., 290; 308 Hollister, C.D., 313; 337 Hollom, P.A.D. See Peterson, R., 599 Holloway, G., 90; 123; See Nazarenko, L., 131 Holloway, M., 536; 589 Holloway, P.E., 47, 53, 102; 123 Holmquist, J.G. See Eckrich, C.E., 583 Holst, G. See Jorgensen, B.B., 226 Holt, J. See Allen, J.I., 111; See Proctor, R., 134 Holt, J.T., 47, 89, 92, 108; 124 Holt, M.W. See Bidlot, J.R., 99, 106; 114 Holt, R.D., 324; 337 Holtan, G. See Berge, J.A., 479 Holthuijsen, L.H. See Booij, N., 114; See Ris, R.C., 134 Hong, B.G., 22; 30; See Sturges, W., 22; 34 Honjo, S., 177, 183, 185, 187, 194, 203, 204, 206; 225, 226; 313; 337; See Beier, J.A., 222; See Fischer, G., 163 Hooker, J.D., 143, 161; 164 Hooker, S.K. See Lucas, Z.N., 527; 594 Hoover, D.J., 328; 337; See Smith, C.R., 230, 340 Hoover, S.E., 517; 589 Hopkins, T.E. See Serafy, J.E., 305 Horn, M.H., 492; 589 Horn, R.C. See Smith Jr, K.L., 230 Horner, R., 144; 164; See Legendre, L., 165 Horner, R.A., 144; 164 Horrill, A.D., 569; 590; See Sanchez, A.L., 602 Horton, C., 107; 124 Hoshai, T. See Horner, R., 164; See Legendre, L., 165 Hoss, D.E. See Govoni, J.J., 298; See Hanson, P.J., 253; 298 Houle, A., 497; 590 Houry, S., 26; 30 Howald, G.R., 523; 590

632

AUTHOR I ND E X

Howard, B.J., 569; 590; See Sanchez, A.L., 602 Howard, D. See Dailey, M.D., 582 Howard, S. See Jones, K., 591 Howarth, M.J., 77; 124; See Jones, J.E., 73, 81, 85, 89; 125; See Jago, C.F., 226 Howarth, R.W. See Peterson, B.J., 228 Howell, R.L. See Livingston, R.J., 301 Howell, W.H. See Jury, S.H., 299 Howitt, L. See Morrisey, D.J., 303 Howorth, J.M. See McKay, W.A., 596 Hsieh, C.K. See Bailey, R.T., 113 Hu, K., 43, 58; 124 Huang, W. See Livingston, R.J., 301 Hubbert, G.D., 85; 124 Hubbert, K.P. See Flather, R.A., 84; 121 Hubbertan, H.-W. See Eicken, H., 163; See Mackensen, A., 227 Hudson, J.H. See Smith, T.J., 306 Hudson, P., 543; 590 Huenefeld, R.E. See Brown, W.K., 579 Huess, V. See McClimans, T.A., 129 Huggett, C.L. See Levin, L.A., 338 Hughes, B. See Green, A.J., 298 Hughes, C.W., 19, 20; 30; See Gille, S.T., 19; 29; See Rintoul, S.R., 33; See Woodworth, P.L., 35 Hughes, D.A., 279; 299 Hughes, J.A. See Gooday, A.J., 336 Hughes, L., 507; 590 Hughes, T.P., 397; 416 Hulberg, L.W. See Oliver, J.S., 339 Hull, S. See Raffaelli, D.G., 600 Hulscher, J.B. See Goss-Custard, J.D., 587 Humayun Kabir, A.B.M. See Roy, G.D., 135 Hummel, H. See Snelgrove, P.V.R., 341 Humphery, J.D. See Williams, J.J., 232 Humphries, P., 280, 281; 299 Humphry, S.R. See Forys, E.A., 570, 571; 586 Hunke, E.C., 81; 124 Hunte, W. See Vermeer, L.A., 424 Hunter, C.L. See Rice, S.A., 269; 304 Hunter, M. See Andrew N.L., 343– 425 Huntley, D.A. See O’Connor, B.A., 131; See Ozanne, F., 132 Hur, S.B., 377; 416; See Andrew N.L., 343– 425; See Yoo, S.K., 425 Hurlbert, S.H., 237; 299; 314; 337; See Greenwald, G.M., 271, 273, 292; 298 Hurst, R. See McClatchie, S., 338 Huston, M., 323; 337

Hutchings, P., 275; 299; 522; 590; See Koop, K., 300 Hutchings, P.A. See Smith, C.R., 340; See Snelgrove, P.V.R., 340; 341 Huthnance, J.M., 45, 50, 69, 85, 88, 97; 124; See Amin, M., 45, 68, 69, 92; 112; See Davies, A.M., 118; See Cartwright, D.E., 116; See Charnock, H., 116 Hutterer, R. See Boye, P., 578 Huybrechts, P. See Church, J.A., 29 Hyde, K.D. See Palmer, M.A., 339 Hydes, D.J., 43, 73; 124; See Howarth, M.J., 124; See Le Gall, A.C., 127 Hyler, W.R., 564; 590 Hylland, K., 567; 590 Hyndes, G.A. See Potter, I.C., 283; 304; See Young, G.C., 309 Iakovlev, N.G., 57; 124 Ibanez, C., 505; 590 Ibàñez, C., 74; 124 Ignatieva, N.V., 441; 482 Ikävalko, J. See Eicken, H., 163 Iken, K., 217; 226 Imamura, F. See Tsuji, Y., 605 Imamura, K., 393, 394; 416 Imber, M., 523; 590 Imber, M.J., 523; 590 Inall, M.E. See Guizen, K., 122 Ince, N.Z. See Croft, T.J., 117 Indrehus, J. See Beyer, F., 467; 479 Ineson, S. See Février, S., 121 Inman, A. See Bazely, D.R., 577 Innes, S. See Furgal, C.M., 586 IOC, 3, 15, 16, 25, 26, 27; 30 Ip, J.T.C., 84; 124; See Lynch, D.R., 128 Irlandi, E., 234, 271, 273, 279; 299 Isaji, T. See Spaulding, M.L., 94; 137 Isdale, P., 268; 299 Isdale, P.J. See Smith, T.J., 306 Iseki, K. See Wong, C.S., 232 Isert, K. See Becker, G.A., 113 Iskandarani, M. See Curchitser, E.N., 117 Iso, S. See Nakano, Y., 303 Ittekkot, V. See Honjo, S., 225; See Rixen, T., 229 Ittekot, V.A.W. See Bruland, K.W., 223 IUCN, 529; 590 Ivanov, B.G., 375; 416 Ivanov, V. See Pelinovsky, E., 132 Ivanov, V.A.S., 53; 124

633

AUTHOR I ND E X

Ivanov, V.F. See Sarkisyan, A.S., 94; 137 Izquierdo, A. See Alvarez, O., 112; See Bruno, M., 115; See Tejedor, L., 138 Jablonski, D. See Roy, K., 340 Jackson, A. See Pain, D.J., 598 Jackson, D.B., 516; 590 Jackson, G. See Wyer, M.D., 608 Jacobs, G.A., 20; 30 Jacobson, M.E. See Fong, P., 297 Jacoby, C., 574; 590 Jacques, G., 152; 164 Jacques, T. See Jauniaux, T., 591 Jacquinet, E. See Jauniaux, T., 590 Jaeger, J.J. See Millien-Parra, V., 495; 596 Jaehmlich, S. See Leipe, T., 227 Jago, C.F., 93; 124; 177, 194, 209; 226 Jahn, A., 472, 473, 475; 482 Jamart, B.M., 84; 124 James, A.R.C., 496; 590 James, C. See Strub, P.T., 204; 231 James, I.D., 46, 47, 48, 49, 51, 69, 70, 71, 86; 125; See Holt, J.T., 47, 89, 92, 108; 124; See Jones, J.E., 125; See Proctor, R., 47, 48, 86, 89, 92, 102, 104; 134; See Souza, A.J., 67, 71; 137 James, W. See Sanderson, J.C., 422 Jameson, R.J. See Bodkin, J.L., 498, 527; 578; See Estes, J.A., 584 Jamieson, G.S., 408; 416; See Orensanz, J.M., 409; 420 Janas, U. See Jahn, A., 482 Jankowski, J.A., 49, 92; 125 Janssen, F., 43; 125 Janssen, P.A.E.M. See de las Heras, M.M., 118; See Komen, G.J., 126 Jansson, R., 239; 299 Jara, F.H. See Moreno, C.A., 419 Jarman, P.J. See Brock, M.A., 295 Järvekülg, A., 441, 443, 446; 483 Jarvis, A.M. See Robertson, A., 601 Jarvis, J.J.U.M. See Narins, P.M., 597 Jassby, A.D., 236; 299 Jauniaux, T., 527, 528; 590 Jazdzewski, K., 498; 591 Jeans, D.R.G., 102; 125 Jefferson, T.A., 528; 591; See Minh, T.B., 596 Jenkins, A.D. See Johannessen, O.M., 125 Jenkins, D. See Harper, R.J., 539; 588 Jenkins, S.R. See Thompson, R.C., 605 Jennings Jr, J.C. See Dieckmann, G.S., 162

Jensen, H.R., 45, 60; 125; See Babaric, V., 112 Jensen, J.N. See Andersin, A.-B., 478; See Josefson, A.B., 463, 473; 482 Jensen, K. See Bach, H.K., 112 Jensen, P. See Lambshead, P.J.D., 338 Jensen, T.G., 45, 64, 87; 125 Jenserud, T. See McClimans, T.A., 31 Jerling, L., 500, 502; 591 Jermakovs, V. See Cederwall, H., 443; 480 Jerome, J.H. See Pozdnyakov, D.V., 133 Jewell, P.A., 559; 591 Ji, M., 16; 30; See McPhaden, M.J., 31; See Xue, Y., 35 Jickells, T.D. See Lampitt, R.S., 227; See Newton, P.P., 228 Jimenez, J.E. See Erwin, R.M., 584 Jimenez-Marrero, N.M. See Mignucci-Giannoni, A.A., 596 JNCC, 571; 591 Jochmann, P. See Eicken, H., 163 Johannessen, B.O. See McClimans, T.A., 31; 129 Johannessen, O.M., 104, 106; 125; See Fennel, W., 82; 121 Johannessen, Ø. See Gray, J.S., 337 Johannessen, T., 466; 482 Johanos, T.C. See Hiruki, L.M., 589 Johansson, B., 472, 474; 482; See Rosenberg, R., 487 John, A.W.G. See Campos-Creasey, L.S., 223; See Rice, A.L., 229, 339 John, K. See Feldmann, J., 585 Johns, B., 45, 49, 76, 86, 94; 125 Johnsen, H. See Breivik, L.-A., 115 Johnson, A.M., 541; 591; See Estes, J.A., 584; See Garshelis, D.L., 586 Johnson, C.R., 384; 416; See Andrew N.L., 343– 425; See Chapman, A.R.O., 401; 412; See Smith, J.S., 566; 603 Johnson, H.K., 44, 100, 101; 125 Johnson, H. See Péchon, P., 132 Johnson, J., 43, 46; 125 Johnson, M.S. See Jones, S.R., 591 Johnson, R.K. See Goedkoop, W., 173, 195, 214; 224; See Palmer, M.A., 339 Johnson, S.P. See Goldstein, T., 587 Johnson, S.R. See Demarchi, M.W., 582 Johnson, T.H. See Green, R.E., 588 Johnson, W.K. See Wong, C.S., 232 Johnston, A.M. See Raven, J.A., 167 Johnston, R.F., 516; 591

634

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Johnston, T.M.S., 22; 30 Joint, I.R. See Howarth, M.J., 124 Joint Global Ocean Flux Study, 185, 201; 226 Jones, A.R., 276; 299 Jones, B., 100; 125 Jones, C.M. See Thorrold, S.R., 307 Jones, G.B. See Koop, K., 300 Jones, G.K., 283; 299 Jones, G.P., 492; 591 Jones, H. See Marshall, J., 129 Jones, J.E., 37–141; 44, 57, 59, 60, 65, 73, 81, 85, 87, 89; 125; See Davies, A.M., 49, 63, 65, 68, 78, 83, 101, 104; 118; See Heaps, N.S., 65; 123; See Howarth, M.J., 124 Jones, K., 566; 591 Jones, K.J. See Rippeth, T.P., 98; 134 Jones, M.B. See Roper, D.S., 305 Jones, R.E. See Hanni, K.D., 588 Jones, S.E. See Jago, C.F., 177, 194; 226; See Prandle, D., 134 Jones, S.R., 569; 591 Jonge, V.N. de, 476; 483 Jonsson, B. See Jonsson, P., 476; 482 Jonsson, P., 428, 430, 444, 445, 446, 468, 476; 482; See Persson, J., 428, 436, 446, 454; 486; See Rosenberg, R., 487 Jorge da Silva, A., 277; 299 Jorgensen, B.B., 208; 226 Jörgenssen, B.B., 476; 483 Josefson, A.B., 458, 462, 463, 465, 467, 473; 482; See Andersin, A.-B., 478; See Pearson, T.H., 486; See Rosenberg, R., 487 Josey, S. See Myers, P.G., 131; See Samuel, S., 135 Josey, S.A., See Tsimplis, M.N., 22; 34 Jouventin, P. See LeCorre, M., 494, 522; 593; See Micol, T., 494, 495; 596 Judge, M.L., 274; 299 Juhlin, B., 455; 483; See Smith, S., 488 Juinio, M.A., 382; 416 Juinio-Meñez, M.A., 382, 383, 394, 395; 416; See Andrew N.L., 343– 425; See Malay, M.C.D., 418 Julian, P. See McPhaden, M.J., 31 Jumars, P.A., 171, 211, 214, 217, 219; 226; 316, 324, 325, 327, 328, 331; 337, 338; See Hessler, R.R., 313, 316, 320, 326; 337; See Smith, C.R., 340; See Yager, P.L., 232 Jumppanen, K., 436, 438, 473; 483; See Bagge, P., 479 Jungclaus, J. See Dickson, B., 29

Jungclaus, J.H., 45, 48, 90; 125; See Simeonov, J., 136 Junge, K. See Krembs, C., 165 Jürss, K. See Röhner, M., 487 Jury, S.H., 273, 279; 299 Justic, D., 253; 299 Jutila, H.M., 504; 591 Kaartvedt, S., 275; 299 Kaas, H. See Conley, D.J., 480 Kadko, D.C. See Stephens, M.P., 231 Kafemann, R. See Thiel, R., 307 Kagan, B.A., 100; 126; See Alvarez, O., 112; See Bruno, M., 115; See Tejedor, L., 138 Kahler, A. See Pfannkuche, O., 229 Kairesalo, T. See Palmer, M.A., 339 Kaiser, G.W. See Hobson, K.A., 589; See Taylor, R.H., 604 Kaldy, J.E. See Short, F.T., 305 Kale, H.W., 525; 591 Kalk, M. See Macnae, W., 492; 594 Kalvass, P., 365, 366, 367, 368, 369, 370, 404; 416; See Andrew N.L., 343– 425 Kalvass, P.E. See Karpov, K.A., 416; See Ebert, T.A., 414 Kambarage, D.M. See Ngomuo, A.J., 597 Kamenkovitch, V.M. See Cane, M.A., 116 Kamermans, P., 256, 258, 262; 299 Kämpf, J., 55, 63, 82, 90, 93, 100; 126 Kamp-Nielson, L. See Terrados, J., 307 Kan, A., 344; 416 Kanda, J. See Kitazato, H., 226 Kangas, P. See Elmgren, R., 481 Kannan, K. See Watanabe, M., 606 Kanneworff, E., 177, 190; 226; See Christensen, H., 177; 223 Kantardgi, I., 101; 126 Kantha, L.H. See Galperin, B., 121; See Horton, C., 124; See Tierney, C.C., 34 Kapel, C.M.O., 532; 591 Karaca, M., 66; 126 Karambas, T. See Péchon, P., 132 Karcher, M.J. See Harms, I.H., 73; 123 Karim, K., 236; 299 Karl, B.J. See Fitzgerald, B.M., 585 Karl, D.M. See Scharek, R., 230 Karlson, K., 427– 489 Karpov, K.A., 368, 370, 398, 401; 416 Kashef, A.I., 287; 299 Kassas, M., 501; 591 Kastendiek, J. See Dixon, J.D., 413

635

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Kathiresan, K., 513, 570; 591 Kato, S., 365, 367, 368, 369, 407; 417 Kato, Y. See Kitazato, H., 217; 226 Kattner, G. See Fahl, K., 159; 163; See Kennedy, H., 165; See Thomas, D.N., 168 Kaufman, A.J. See Hoffman, P.F., 164 Kaufman, L. See Suzaki, A., 604 Kaufmann, R.S., 217; 226; See Lauerman, L.M.L., 181, 189, 207, 216; 227; See Smith, K.L., 230, 231; 325, 334; 340; See Smith Jr, K.L. Kauker, F., 47; 126 Kauppila, P., 433, 438, 439, 440; 483; See Pitkänen, H., 486 Kautsky, H., 428; 483 Kautsky, L., 431; 483 Kautsky, N. See Kautsky, L., 431; 483 Kawabe, M., 21; 30 Kawai, T. See Agatsuma, Y., 405; 410 Kawamata, K. See Agatsuma, Y., 410 Kawamata, S., 396; 417 Kawamura, K., 344, 353, 354, 355, 357, 396; 417 Kawana, K., 396; 417 Kawata, Y. See Tsuji, Y., 605 Kay, D. See Wyer, M., 607; See Wyer, M.D., 607 Kay, F.R. See Whitford, W.G., 507; 607 Kearney, M.S., 492; 591 Keedwell, R. See Moller, H., 596 Keegan, D.R., 494; 591 Keene, J.B. See Harris, P.T., 298 Keesing, J., 346, 374, 389; 417 Keil, R.G. See Good, E.I., 224 Keiper, R. See Bashore, T.L., 576 Keiper, R.R., 556, 561; 591 Keller, V.E., 536; 591 Kelly, S. See Babcock, R.C., 410 Kelly, S.J. See Rippingale, R.J., 272, 274, 278; 305 Kelly-Gerreyn, B.A. See Le Gall, A.C., 127; See Hydes, D.J., 124 Kelsey, R., 493; 591 Kemp, A.E.S., 203, 218, 219; 226 Kemp, W.M. See Boynton, W.R., 294; See Day Jr, J.W., 582 Kench, P.S., 239, 248, 250; 299 Kendrick, T.H. See Breen, P.A., 403; 411 Kennedy, H., 165; See Herborg, L.-M., 164; See Thomas, D.N., 168 Kennedy, S. See Diguardo, G., 582

Kennelly, S.J., 268; 299 Kenworthy, W.J., 262; 299; See Duarte, C.M., 296; See Fonseca, M.S., 585; See Powell, G.V.N., 304; See Terrados, J., 307 Kenyon, K.W. See Simenstad, C.A., 422 Kenyon, R.A. See Vance, D.J., 308 Keough, M.J., 292; 300 Kerley, G.I.H., 497; 565, 571; 592 Kerr, J.D. See Vance, D.J., 308 Kesner, K.P. See Talaue-McManus, L., 382, 383; 423 Keyes, M.C., 553; 592 Khan, T.A., 246, 287; 300 Khan, T.M.A., See Singh, O.P., 33 Khlebovich, V.V., 567; 592 Khripounoff, A. See Auffret, G., 222; See Desbruyères, D., 336; See Vangriesheim, A., 175, 187; 231 Khurana, K.K. See Kivelson, M.G., 165 Kidd, R., 275; 300 Kiehl, K., 502; 592 Kiely, O., 492, 548; 592 Killworth, P.D., 59, 62, 66, 82, 89; 126; See Woodgate, R.A., 104; 140 Kilonsky, B., 4, 5; 31; See Merriﬁeld, M., 32; See Mitchum, G., 32 Kilonsky, B.J., 4, 13; 31; See Soreide, N.N., 33 Kilpi, M., 545; 592 Kimball Jr, J.F., 193, 194; 226 Kimmerer, W.J. See Jassby, A.D., 299 Kimura, M. See Suzaki, A., 604 Kimura, S. See Suzaki, A., 604 Kindle, J.C. See Haidvogel, D.B., 123 King, A.J. See Humphries, P., 299 King, C. See Shum, C.K., 33 King, C.K., 390; 417 King, G. See Snelgrove, P.V.R., 341 King, G.M. See Smith, C.R., 340; See Snelgrove, P.V.R., 340 King, J.E., 552, 553; 592; See Harrison, R.J., 553, 555; 589 King, J.M. See Eldridge, M.D.B., 584 King, P. See Lampitt, R.S., 227; See Newton, P.P., 228 King, R.J. See West, R.J., 300 King, S. See Zino, F., 608 Kingsford, M.J., 240, 246, 247, 249, 250, 252, 280, 285, 286; 300; See Dorfman, E.J., 286; 296; See Gillanders, B.M., 233–309; 253; 297; See Grimes, C.B., 234, 236, 237, 246, 247, 249, 250, 252, 271, 278, 285; 298

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Kingsford, R.T., 234, 235, 236, 286; 300; See Lemly, A.D., 301 Kinnison, M.T. See Jury, S.H., 299 Kinoshita, T., 353; 417 Kinsey, R.B. See Crooks, K.R., 581 Kiørboe, T., 206, 211; 226 Kirby, J.T. See Chawla, A., 53; 116; See ÖzkanHaller, H.T., 85; 132 Kiriakoulakis, K. See Lampitt, R.S., 227 Kirkkala, T., 438; 483 Kirkman, H. See McComb, A.J., 302 Kirkpatrick, J.F. See Bashore, T.L., 576 Kirkwood, J.K. See Kuiken, T., 593 Kirschvink, J.L. See Gaidos, E.J., 163 Kirst, G.O., 144, 145, 150; 165; See Gleitz, M., 150; 163; See Malin, G., 150; 165; See Wolfe, G.V., 169 Kit, E. See Sladkevich, M., 137 Kitada, S., 394, 395; 417 Kitazato, H., 183, 187, 190, 192, 202, 207, 209, 215; 226; See Odum, E.P., 183, 209; 228 Kitching, J.A., 406; 417; See Muntz, L., 419 Kitheka, J.U., 251, 252; 300 Kivelson, M.G., 144; 165 Kiviniemi, K., 565, 571; 592 Kivman, G.A. See Kurapov, A.L., 104; 126 Kjerfve, B. See Moore, W.S., 131 Kjerve, B. See Gardner, L.R., 586 Klein, C.J. See Livingston, R.J., 301 Klein, D.R., 562; 592 Kleine, E., 81; 126; See Funkquist, L., 45, 105, 108; 121 Kleivane, L., 546; 592 Klevanny, K.A. See Androsov, A.A., 112 Kliem, N., 48, 63; 126; See McClimans, T.A., 129 Klimstra, W.D., 500; 592 Klindworth, C. See Mackensen, A., 227 Klingeborn, B. See Esfandiari, J., 537; 584 Klinger, T.S., 396; 417 Klinting, A. See Babaric, V., 112 Klopatek, C. See Brussaard, L., 335 Klosko, S. See Shum, C.K., 33 Klosko, S.M. See Tapley, B.D., 34 Klunsoyr, J. See Boon, J.P., 578 Knauer, G.A. See Fowler, S.W., 172, 192; 224 Knight, P.J. See Prandle, D., 134 Knighton, A.D., 570; 592 Knoop, W.T. See Adams, J.B., 293; See Talbot, M.M.B., 307 Knowlton, N., 318; 338

Koblinsky, C.J. See Fu, L.-L., 29 Kocatas, A. See Le Direac’h, J.P., 418 Koch, E.W., 260, 264; 300 Kocher, T.D. See France, S.C., 318; 336 Kochergin, V.P., 75, 77; 126 Kochman, H.I. See Buckingham, C.A., 579 Kochnev, A.A. See Ovsyanikov, N.G., 598 Koehn, J.D. See Humphries, P., 299 Kofoed-Hansen, H. See Johnson, H.K., 125 Kögler, J., 97; 126 Kogut, J. See Valk, H., 500; 605 Koike, I. See Smith, C.R., 340; See Snelgrove, P.V.R., 340, 341 Kojima, H., 395; 417 Kok, A. See de Wilde, P.A.W.J., 223; See Duineveld, G.C.A., 224 Kolb, H.H., 519; 592 Kolesar, S.E. See Breitburg, D.L., 479 Komen, G.J., 99; 126 Konai, M., 492; 592 Kondrachoff, V. See Estournel, C., 120; See Marsaleix, P., 129 Kondratyev, K. Ya. See Pozdnyakov, D.V., 133 Kondratyev, S. See Pitkänen, H., 486 König, P. See Becker, G.A., 133; See Pohlmann, T., 133; See Puls, W., 134 Kononen, K. See Pavelson, J., 132 Konopacka, A. See Jazdzewski, K., 498; 591 Koop, B.F. See Byun, S.A., 579 Koop, K., 269; 300; See Larkum, A.W.D., 268; 300 Koponen, J. See Leppänen, J.-M., 484 Korean Fishery Association, 377; 417 Kores, G. See Pinardi, N., 133 Kormas, K.A., 181; 226 Korostelev, V.G. See Bertram, V., 114 Korotenko, K.A., 74; 126; See van Dam, G.C., 139 Korsun, S. See Forman, S.L., 586 Kortenkamp, A., 567; 592 Koseff, J.R. See Lucas, L.V., 301 Koski, M. See Gulland, F.M.D., 588 Kotta, I. See Kotta, J., 443; 483; See Ojaveer, H., 485 Kotta, J. See Ojaveer, H., 485 Kotze, P.G.H. See Oosthuizen, W.H., 598 Kourafalou, V.H., 86; 126 Kouts, T. See Talipova, T.G., 138 Kovacs, K., 553; 592 Kovacs, K.M. See Furgal, C.M., 586 Kowalewski, M., 245; 300

637

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Kozakiewicz, A. See Dobrowolski, K.A., 582 Kraidees, M. See Abouheif, M.A., 575 Kramer, K.J.M. See Laane, R.W.P.M., 126 Kranenburg, C. See Bijvelds, M.D.J.P., 114 Kraufvelin, P., 438; 483 Krell, A. See Mock, T., 166 Krembs, C., 147, 150, 151, 158, 161; 165; See Eicken, H., 163 Kremer, J.N. See Fritsen, C.H., 163 Kretzmann, M.B., 495; 592 Kreyscher, M. See Bornemann, H., 578 Kristan, W.B. See Chase, M.K., 580 Kristensen, E., 473; 483; See Andersen, F.Ø., 476; 478 Kristensen, P. Kronvang, B., 300 Kristiansen, S., 153, 155; 165; See Syvertsen, E.E., 152, 153; 168 Kritsuk, S.G. See Gorny, V.I., 122 Kritzinger, J.J. See van Aarde, R.J., 605 Krogh, M.G., 494; 592 Krogstad, H.E. See Breivik, L.-A., 115 Kronvang, B., 243; 300 Krupiysky, A. See Cane, M.A., 116 Kruse, G.H. See Woodby, D., 425 Kruuk, H., 513, 536, 537, 538, 539, 540, 566, 567, 571; 592; See Nolet, B.A., 540; 597 Krystufek, B., 533; 593 Kube, J., 451, 452, 468, 474; 483; See Powilleit, M., 451, 452, 468; 486 Kübler, J. See Raven, J.A., 167 Kudryavtsev, V.N., 101; 126; See Makin, V.K., 101; 129 Kueh, C.S.W., 553; 593 Kühl, M., 149; 161; 165; See Rysgaard, S., 167 Kuhn, M. See Church, J.A., 29 Kuhnlein, H.V., 567; 593 Kuiken, T., 527; 593 Kukert, H., 316, 320, 328, 330, 331; 338; See Gleitz, M., 164; See Smith, C.R., 320; 340 Kuo, J. See McComb, A.J., 302 Kuosa, H. See Eicken, H., 163; See Ennet, P., 120; See Kristiansen, S., 165 Kurapov, A.L., 104; 126 Kurtonur, C. See Krystufek, B., 593 Kushner, D. See Karpov, K.A., 416 Kutarski, P. See Kueh, C.S.W., 593 Kutiel, P., 505; 593 Kuylenstierna, M. See Tiselius, P., 202; 231 Kwong, S.C.M. See Davies, A.M., 65; 118

Laane, R.W.P.M., 95; 126; See Berlamont, J., 114 Lääne, A. See Pitkänen, H., 486 Laanearu, J., 53; 126 Laanemets, J. See Pavelson, J., 132 Labrosse, A. See Vested, H.J., 139 Lacroix, G., 98; 127 Laffaille, P., 493, 505; 593 Lafuente, J.G., 52; 127 Lagzdins, G. See Andersin, A.-B., 478; See Cederwall, H., 480 Lahoz, M.G. See Albiach, J.C.C., 111 Lai, H.L., 356, 387, 405, 407; 417 Laine, A. See Zorita, E., 469; 489 Laine, A.O., 431, 439, 441, 444, 446, 447, 472, 474; 483 Lajtha, K. See Valiela, I., 307 Lake, S. See Palmer, M.A., 339 Lalas, C. See Bradshaw, C.J.A., 579 Laliberte, C. See Dewailly, E., 582 Lam, F.-P. A., 51, 87; 127 Lamb, C.S. See Howard, B.J., 590 Lambeck, K. See Church, J.A., 29 Lambeck, R.H.D. See Goss-Custard, J.D., 587 Lambin, X. See Telfer, S., 604 Lambrigtd, D. See Jauniaux, T., 590 Lambshead, P.J.D., 313, 315, 317, 318, 322, 324, 326, 329, 334; 338; See Boucher, G., 315, 315; 335; See Eckman, J.E., 336; See Gooday, A.J., 224, 336; See Rice, A.L., 328, 329; 339; See Smith, C.R., 340; See Snelgrove, P.V.R., 340, 341 Lamont, P.A. See Gage, J.D., 336; See Levin, L.A., 338; See Paterson, G.L.J., 339 Lampitt, R. See Bruland, K.W., 223 Lampitt, R.S., 175, 190, 200, 202, 203, 205, 206, 207, 209, 210; 226, 227; See Billett, D.S.M., 222, 335; See Newton, P.P., 228; See Rice, A.L., 229, 339 Lancaster, V. See Smith, P.A., 603 Lancelot, C., 43, 97; 127; See Tusseau, M.-H., 139 Lander, R.H., 553; 593 Landner, L. See Rosenberg, R., 487 Landry, T., 520; 593 Lane, A., 67, 103; 127; See Gerritsen, H., 121; See Prandle, D., 91; 134 Lange, C.B. See Kemp, A.E.S., 226 Lange, M.A., 145; 165; See Dieckmann, G.S., 162; See Eicken, H., 163 Lange, U. See Greve, W., 54, 107; 122

638

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Langenberg, H., 80, 86, 90; 127; See Becker, G.A., 113 Langlais, M. See Courchamp, F., 581 Lankov, A. See Ojaveer, H., 485 Lännergren, C., 436, 453; 483 Lanyon, J.M., 554; 593 Laprise, R., 275; 300 Lara, R.J., 158; 165; See Thomas, D.N., 168 Lara-Dominguez, A.L. See Yanez-Arancibia, A., 608 Large, W.G., 45, 75, 79; 127 Largier, J.L. See Botsford, L.W., 411; See Wing, S.R., 425 Larkin, P.A., 407; 417 Larkum, A.W.D., 263, 268; 300; See Koop, K., 300; See West, R.J., 308 La Rocca, N. See Andreoli, C., 162 Larsen, H.J. See Skaare, J.U., 603 Larsen, S.E. See Johnson, H.K., 125; See Kronvang, B., 300 Larson, R. See Woodby, D., 425 Larson, R.H. See Woodby, D., 425 Larsson, U., 428, 444; 484; See Elmgren, R., 436, 453, 468; 481 Larter, N.C., 566; 593 Lascaratos, A. See Drakopoulos, P.G., 81; 120; See Nittis, K., 90; 131; See Pinardi, N., 133 Lass, H.U. See Schmidt, M., 135 Lassig, J. See Andersin, A.-B., 478; See Elmgren, R., 481 Last, P.R. See Edgar, G.J., 296 Latasa, M. See Stephens, M.P., 231 Latkoczy, C. See Thorrold, S.R., 307 Latypov, I. Sh. See Gorny, V.I., 122 Lauerman, L.M.L., 181, 189, 201, 207, 216, 217; 227 Launder, B.E. See Croft, T.J., 117 Laureillard, J. See Pinturier-Geiss, L., 229 Lauridson, C.P.L. See Sloan, N.A., 422 Lavaleye, M.S.S. See de Wilde, P.A.W.J., 223; See Duineveld, G.C.A., 224 Lavelle, P. See Brussaard, L., 335 Lavergne, Y. See Himmelman, J.H., 415 Lavigne, D.M. See Malik, S., 594 Lavin, M.F., 241, 242, 245; 301 Lavrenov, I.V. See Rozhkov, V.A., 135 Law, B. See Lunney, D., 594 Law, R.J. See Boon, J.P., 578 Lawler, I.R., 573; 593 Lawrence, A.L. See Klinger, T.S., 417

Lawrence, B. See Cadwallader, P., 241, 242, 281; 595 Lawrence, J. See Davies, A. M., 101; 118; See Ozanne, F., 132 Lawrence, J.H., 346, 393, 398, 404, 405; 417; See Klinger, T.S., 417 Lawrence, M.J., 545, 546, 566; 593 Lay, D.W. See Lynch, J.J., 594 Layna, J.F. See Gazo, M., 587 Lazier, J.R.N. See Mann, K.H., 204; 228; 251; 302 Lazier, J.R.N. See Malone, T.C. Laznik, M., 442; 484 Lazure, P. See Tartinville, B., 138 Lazzara, L. See Guglielmo, L., 164 Leakey, R.J.G. See Archer, S.D., 162 Leaman, K. See Castellari, S., 116 Leary, T. See Lunney, D., 594 Leber, K.M., 395; 417 LeBlanc, W.G. See Prince, J.S., 382; 420 Le Boeuf, B.J. See Worthy, G.A.J., 607 Leckie, J.O. See Hansen, A.M., 92; 123 Leclerc, M. See Heniche, M., 123 LeCorre, M., 494, 522; 593 Le Corre, P. See Sourna, A., 306 Le Direac’h, J.P., 384, 390, 392; 418 Lee, C., 179, 188; 227; See Sun, M.-Y., 231 Lee, J.C., 76, 84; 127 Lee, K.N., 293; 301 Lee, K.-S., 259; 301 Lee, M. See Samelius, G., 532; 601 Lee, T.N. See Kourafalou, V.H., 126 Lee, T. See Lundquist, C.J., 418 Lee Long, W.J. See Preen, A.R., 304 Leendertse, P.C. See Bakker, J.P., 576 Leetmaa, A. See Ji, M., 30; See Xue, Y., 35 Lefebre, K. See Scholin, C.A., 602 Lefebvre, L.W., 554; 593; See Buckingham, C.A., 579 Lefeuvre, J.-C. See Laffaille, P., 593 Lefèvre, F., 24; 31; 87, 127; See Dorandeu, J., 29 Lefèvre, J.-M. See Fradon, B., 121 Le Gall, A.C., 44, 73; 127 Le Gall, P., 390, 392; 418 Legendre, L., 144; 165; 201; 227; See Horner, R., 164 Leggett, D.J. See Möller, I, 130 Legzdina, M. See Andrushaitis, A., 478 Le Hir, P. See Brenon, I., 48; 115 Lehman, S.M., 564; 593

639

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Lehmann, A., 82, 90; 127 Lehnhausen, W.A. See Quinlan, S.E., 530; 600 Lehtoranta, J., 441; 484 Leipe, T., 211; 227; See Loefﬂer, A., 227; See Witt, G., 232 Leitao, P. See Gomez-Gesteira, M., 122 Leitão, P. See Martins, F., 129 Leithold, L.E. See Levin, L.A., 338 Lellouche, J.-M., 105; 127 Lemke, P. See Harder, M., 123; See Hilmer, M., 82; See Steiner, N., 138 Lemly, A.D., 235; 301 LeMoign, T. See Sibuet, M., 230 Lenanton, R.C.J., 282; 301; See Loneragan, N.R., 301; See Potter, I.C., 304 Lenhart, H.-J., 74; 127 Lenshs, E. See Andrushaitis, A., 478 Leonard, B.P., 48, 71; 127 Leonard, G.H. See Eicken, H., 163 Leonards, P.E.G. See Boon, J.P., 578 Leonzio, C., 498; 593 Le Pape, O., 44, 97; 127 Lepart, J. See Mesleard, F., 596 Lépez, I. See Moreno, C.A., 419 Leppäkoski, E., 431, 434, 438, 448, 449, 472, 475; 484; See Bagge, P., 479; See Elmgren, R., 481; See Kraufvelin, P., 483; See Olenin, S., 431; 485 Leppänen, J.-M., 439, 442; 484 Lepparanta, M., 81; 127 Leppäranta, M. See Haapala, J., 82; 122 Leppäranata, M. See Omstedt, A., 132 Le Provost, C., 4, 23, 24; 31; 46, 57, 104; 127; See Cazenave, A., 28; See Lefèvre, F., 31, 127; See Meincke, J., 130; See Mitchum, G.T., 32; See Ponchaut, F., 32; See Shum, C.K., 33; See Warrick, R.A., 34; See Woodworth, P.L., 1–35; 35 Lercari, D., 279; 301 Lermusiaux, P.F.J., 45; 128 Leseberg, A., 492; 593 Leslie, W.G. See Robinson, A.R., 229 Lessnau, R.G. See Lehman, S.M., 593 Lester, P.J. See McClatchie, S., 338 Le Traon, P.Y. See Ducet, N., 120; See Ross, T., 33 Leus, K., 566; 593 Leuthold, W., 496; 593 Levasseur, M.E. See Thompson, P.A., 168 Le V. dit Durell, S.E.A. See Goss-Custard, J.D., 587

Leveau, M., 249; 301; See Sournia, A., 306 Leventer, A. 145, 156; 165; See DiTullio, G.R., 224; See Dunbar, R., 160; 162 Levin, L., 215, 219; 227; See Schaff, T., 340; See Wishner, K., 232 Levin, L.A., 179, 213, 214, 215; 227; 313, 315, 316, 326, 328, 329, 330, 331, 332; 338, 342; See Blair, N.E., 222; See Gooday, A.J., 224, 336; See Schaff, T.R., 328, 331; 340; See Tegner, M.J., 399, 401; 423 Levin, P.S., 398; 418 Levitan, D.R., 403, 404; 418 Lévy, M., 94; 128 Lewis, E.R. See Narins, P.M., 597 Lewis, F.G. See Livingston, R.J., 301 Lewis, J.B., 392; 418 Lewis, J.R., 492; 593 Lewis, S.K. See Porter, J.W., 304 Ley, J.A. See McIvor, C.C., 302; See Montague, C.L., 256; 303 Leznicka, B. See Dobrowolski, K.A., 582 Li, H. See Bailey, R.T., 113 Li, X. See Shum, C.K., 33 Li, Z., 77; 128; See Davies, A.G., 92, 94; 117; See Elliott, A, J., 67; 120; See Pham, M.K., 133 Liberman, Y.M. See Androsov, A.A., 112 Liden, K. See Angerbjorn, A., 575 Lidicker Jr, W.Z. See Hays, W.S.T., 516, 525, 526; 589 Lie, E. See Skaare, J.U., 603 Lifjeld, J.T., 492; 593 Liljekvist, J. See Perus, J., 486 Lim, C.P., 358, 359; 418 Limbaugh, C., 496, 540, 551; 593 Limberger, D., 552; 594 Limburg, K. See Costanza, R., 295 Lin, B., 44, 59, 63, 85; 128 Lin, D. See Bazely, D.R., 577 Lindell, A. See Bonsdorff, E., 479 Lindeman, K.C. See Serafy, J.E., 305 Lindemann, F. See Eicken, H., 163 Lindesay, J. See Allan, R., 294 Lindesjöö, E. See Smith, S., 488 Lindquist, K. See Smith, S., 488 Lindqvist, K., 455; 484 Lindstrom, E.R. See Bevanger, K., 577 Line, M.A., 549; 594 Linke, P., 210; 227; See Gooday, A.J., 224; See Graf, G., 225 Linn, I., 545; 594

640

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Linnell, J.D.C. See Andersen, R., Linthurst, R.A. See Reimold, R.J., 600 Linton, L.R. See Brown, W.K., 579 Lips, U. See Laanearu, J., 126 Lipscomb, D.J. See Gardner, L.R., 586 Lipscomb, T. See Scholin, C.A., 602 Lirman, D. See Fong, P., 297 Liss, P.S. See Charnock, H., 116 Littlejohn, R.P. See Clark, R.G., 580 Litvaitis, J.A. See Miller, B.K., 500; 596 Liu, C. See Lu, N., 301 Liu, Y.P., 97; 128 Livens, F.R. See Howard, B.J., 590 Livingston, H.D. See Staneva, J.V., 137 Livingston, R.J., 278, 279, 282; 301; See Meeter, D.A., 302 Lizotte, M. See McMinn, A., 166 Lizotte, M.P., 144, 148, 152, 156, 159; 165; See DiTullio, G.R., 224; See Priscu, J.C., 167 Llano, M. See Thunell, R.C., 231 Llewellyn, C. See Billett, D.S.M., 222 Lloyd, P.M. See Stansby, P.K., 58; 138 Loch, S. See Prandle, D., 134 Lochet, F. See Leveau, M., 301 Lochte, K., 177, 192, 194, 195, 211, 212; 227; See Boetius, A., 212; 223; See Gleitz, M., 224; See Grossmann, S., 164; See Pfannkuche, O., 177, 206, 211; 228; See Thiel, H., 231, 341; See Turley, C.M., 210, 212; 231 Lockwood, D.R. See Botsford, L.W., 411; See Lundquist, C.J., 418; See Wilen, J.E., 425 Lockyer, C.H. See Kuiken, T., 593 Lode, T., 545, 572; 594 Loewenthal, D. See Leseberg, A., 593 Löfﬂer, A., 179, 200, 201; 227; See Leipe, T., 227 Loganathan, B.G. See Watanabe, M., 606 Loigu, E. See Pitkänen, H., 486 Lomas, M.W., 154, 155; 165 Lomeli, D.J.Z. See Yanez-Arancibia, A., 608 Loneragan, N. See Abal, E.G., 293 Loneragan, N.R., 235, 277, 282, 283, 284, 287; 301; See Longstaff, B.J., 301; See Potter, I.C., 304; See Vance, D.J., 308 Long, A.J. See Raper, S.C.B., 33 Long, D.J., 551; 594; See Hanni, K.D., 588 Long, W.J.L. See Udy, J.W., 307 Longstaff, B.J., 259, 262; 301 Longuet-Higgins, M.S. See Caldwell, D.R., 115 Lonne, I., 528; 594

Loo, L.-O., 460; 484; See Baden, S.P., 478; See Rosenberg, R., 460; 487 Loonen, M.J.J.E. See van der Wal, R., 606 Lopatukhin, L.I. See Rozhkov, V.A., 135 Lopez, D. See Lopez, J.C., 528; 594 Lopez, J.C., 528; 594 López, C. See Álvarez, A., 111; See Stotz, W., 423 López, S. See Lozano, J., 418; See Palacín, C., 420; See Turon, X., 423 Lopez Martinez, N. See Boye, P., 578 Losada, I.J. See Mendéz, F.J., 130 Losada, M.A. See Mendéz, F.J., 130 Loscutoff, S. See Scholin, C.A., 602 Losson, B. See Jauniaux, T., 591 Loupis, A.K. See Eldridge, M.D.B., 584 Loutit, R. See Avery, G., 576; See Nel, J.A.J., 597 Louttit, G.C. See Mackas, D.L., 249; 302 Louw, C.J., 535; 594 Lovalvo, D. See Luther, G.W., 227 Lovejoy, S. See Marguerit, C., 129 Lovell, C.R. See Palmer, M.A., 339 Lowe-McConnell, R.H., 281; 301 Lowenstine, L.J. See Beckmen, K.B., 577; See Dailey, M.D., 582; See Gulland, F.M.D., 588; See Scholin, C.A., 602 Lowry, L.F., 541; 594 Lowry, R.K. See Howarth, M.J., 124 Løyning, T.B., 90; 128 Lozano, J., 348; 418 Lu, N., 273, 278; 301 Lubchenko, J. See Vitousek, P.M., 606 Lubinski, D. See Forman, S.L., 586 Lucas, L.V., 270; 301 Lucas, Z.N., 527; 594 Lucking, R. See Thorsen, M., 605 Lucking, V. See Thorsen, M., 605 Ludwig, D. See Hilborn, R., 292; 298, 415 Luettich, R.A. See Fortunato, A.B., 121; See Westerink, J.J., 140 Luettich Jr, R.A., 58; 128 Luff, R., 74; 128 Lukas, R. See Potemra, J.T., 33 Lukatelich, R.J., 250, 270, 271; 302; See McComb, A.J., 243; 302 Luketina, D., 67; 128 Lumberg, A. See Ojaveer, H., 485 Lundberg, A. See Rosenberg, R., 487 Lundberg, P. See Laanearu, J., 126 Lundgreen, U. See Pfannkuche, O., 228

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Lundquist, C.J., 404, 405; 418; See Morgan, L.E., 419 Lunney, D., 522; 594 Luo, W. See Monbaliu, J., 130 Luong, P. See Ly, L.N., 62; 128 Luthardt, H., 79; 128 Luthcke, S.B. See Tapley, B.D., 34 Luther, D.S., 20; 31; See Chave, A.D., 28; See Filloux, J.H., 29 Luther, G.W., 208; 227 Luttich, S.N. See Schaefer, J.A., 562; 602 Luyten, P. See Davies, A.M., 118; See Deleersnijder, E., 75; 119 Luyten, P.J., 43, 51, 55, 67, 75, 76, 79, 82, 85, 86, 97; 128; See Ruddick, K.G., 135 Ly, L.N., 62; 128 Lyard, F. See Lefèvre, F., 31; See Le Provost, C., 31; 127; See Ponchaut, F., 32 Lyard, F.H., 24, 31, 46, 57; 128; See Lefèvre, F., 127, 128 Lydersen, C. See Kovacs, K., 592 Lyell, C., 497; 594 Lynam, A.J. See Chase, M.K., 580 Lynch, D.R., 48, 51, 58; 128; See Haidvogel, D.B., 123; See Ip, J.T.C., 124 Lynch, J.J., 501, 505; 594 Lyngby, J.E. See Bach, H.K., 112 Lysiak-Pastuszak, E. See Trzosinska, A., 450; 488 Lyslo, A. See Kearney, M.S., 591 Lytle, V.I. See Fritsen, C.H., 163 Lyver, P.O’B., 536, 546; 594 Ma, C.-C. See Chassignet, E.P., 116 MacAlister, H.E. See Pugh, P.J.A., 508; 600 Macawaris, N.D. See Juinio-Meñez, M.A., 416 Macdonald, A., 538, 543; 594 Macdonald, A.A. See Leus, K., 593 MacDonald, D.W., 532; 594; See Brown, L., 532, 533; 579; See Clode, D., 545; 580; See Ferreras, P., 543; 585; See Hersteinsson, P., 531, 532; 589; See Ward, J.F., 606 MacDonald, N.J., 93, 102, 110; 129; See Williams, J.J., 140 MacDonald, S.M. See Mason, C.F., 538, 569; 595 Mace, P.M., 370; 418 MacGinitie, G.E., 554; 594 MacGinitie, N. See MacGinitie, G.E., 554; 594 Macia, S. See Irlandi, E., 299 Macías, J. See Février, S., 121

MacIntyre, A.M. See Robinson, S.M.C., 373, 374; 421 Macintyre, S., 203; 227 Maciolek, N.J. See Grassle, J.F., 313, 314, 316, 317, 318, 319, 320, 321, 322, 327, 331, 332; 337 Mackas, D.L., 249; 302 Mackensen, A., 173, 185, 200, 205; 227 MacKenzie, N.A. See Salter, R.E., 564; 601 Macko, S.A. See Smith, C.R., 340 Macnae, W., 492; 594 MacPhail, R.I. See Bates, M.R., 577 Madec, G., 46; 129; See Lévy, M., 128; See Roullet, G., 47, 81; 135 Madsen, J., 531; 594 Madsen, P.A. See Agnon, Y., 111; See Schäffer, H.A., 53; 135 Madureira, L.A.S. See Conte, M.H., 223 Magaard, L. See Smith, C.R., 230 Maghazy, S.M. See Moubahser, A.H., 597 Magnusson, J., 467; 484; See Berge, J.A., 479 Magnusson, W.E., 522; 594 Maguire, D. See Barnes, D.K.A., 411 Mahamod, Y. See Jago, C.F., 93; 124 Maher, W. See Wasson, B., 308 Maheshwari, B.L., 241; 302 Mahoney, J.B., 179, 187, 195, 203, 207, 209; 227 Maidana, A.M., 69; 129 Main, W. See Grange, K.R., 298 Majluf, P., 572; 594 Mäkelä, K. See Perttilä, P., 486 Makhlouf, M. See El Bahri, L., 584 Makin, V.K., 101; 129; See Kudryavtsev, V.N., 126 Mäkinen, A. See Vahteri, P., 488 Malabib, V., 49, 60; 129 Malanotte-Rizzoli, P. See Napolitano, E., 131; See Oguz, T., 131 Malay, M.C.D., 395; 418 Malcherek, A. See Jankowski, J.A., 125 Malchow, H., 95; 129 Malik, S., 492; 594 Malin, G., 150; 165 Mallick, S.A., 570; 594; See Driessen, M.M., 583 Mallin, M.A., 270, 271; 302 Mallinson, J., 511, 524, 538, 546, 548, 560, 569; 594 Malloch, D.W. See Brussaard, L., 335 Mallory, F.F., 527; 595 Malone, D. See Miller, M.W., 303

642

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Malone, T.C., 194; 228; 270; 302 Malosse, C. See Arnould, C., 575 Malschaert, H. See van Raaphorst, W., 231 Malzone, M.G. See Bett, B.J., 222 Mamaev, L. See Barrett, T., 576 Mañanes, R. See Alvarez, O., 112; See Bruno, M., 118 Mancuso-Nichols, C. See Nichols, D.S., 166 Mandal, M.M. See Roy, G.D., 135 Manganini, S.J. See Honjo, S., 225, 226 Mangel, M. See Cooper, A.B., 404; 413 Mangoni, O. See Guglielmo, L., 164 Mankowski, W. See Demel, K., 447; 480 Mann, K.H., 204; 228; 251; 302; 401; 418; See Breen, P.A., 381, 403; 412; See Johnson, C.R., 384; 416; See Miller, R.J., 382; 419 Manning, C. See Jacoby, C., 590 Manning, R.J.G. See Potter, I.C., 304 Manriquez, P. See Castilla, J.C., 412 Mansﬁeld, A.W., 514; 595 Mansour, M.P. See Volkmann, J.K., 169 Mantoura, R.F.C. See Billett, D.S.M., 222, 335; See Harris J.R.W., 123; See Rice, A.L., 229, 339 Mantoura, R.F.G. See Thiel, H., 231, 341 Mapstone, B.D. See Andrew, N.L., 236; 294; See Keough, M.J., 292; 300 Marais, J.F.K., 283, 284; 302 Marba, N., 257; 302; See Vermaat, J.E., 308 Marchesiello, P., 87; 129; See Barnier, B., 113 Marchington, R.L. See Gassett, J.W., 586 Marcos, F. See Benoit, M., 113 Marguerit, C., 94; 129 Mari, X. See Kiørboe, T., 226 Marietta, M.G. See Dietrich, D.E., 119 Marin, R. See Scholin, C.A., 602 Markovets, I. See Pitkänen, H., 486 Marques, J.C. See Martins, I., 302 Marquet, P.A. See Bozinovic, F., 498; 578 Marsaleix, P., 49, 63, 86; 129; See Auclair, F., 112; See Estournel, C., 120; See Johns, B., 125; See Pinazo, C., 133 Marsh, H. See Heinsohn, G.E., 589; See Lanyon, J.M., 554; 593; See Preen, A., 555, 570; 599 Marsh, R., 89; 129 Marshall, B.E. See Butler, J.R.A., 542; 579 Marshall, D. See Adcroft, A., 84; 111 Marshall, D.P. See Roberts, M.J., 73; 134 Marshall, J., 55; 129; See Adcroft, A., 111; See McCulloch, M., 302

Marshall, J.A. See Tapley, B.D., 34 Marshall, T.C. See Pemberton, J.M., 599 Marsili, L., 498; 595 Martin, A.R., 527; 595 Martin, C. See Levin, L., 227; See Levin, L.A., 227 Martin, C.M. See Levin, L.A., 338 Martin, G. See Alimov, A.F., 478 Martin, J.M. See Pham, M.K., 133 Martin, J.-M. See Denis-Karaﬁstan, A., 119; See Tusseau, M.-H., 139 Martin, K. See Horn, M.H., 589 Martin, T. See Bornemann, H., 578 Martin, W.R., 185, 205, 208; 228 Martin-Bouyer, L. See Bourel, B., 578 Martin-Gonzalez, E. See Castillo, C., 580 Martin-Jézéquel, V., 155, 156; 166 Martins, F., 46, 59, 65; 129 Martins, I., 264, 265; 302 Martinsen, E.A., 46; 129; See Engedahl, H., 120 Martuscelli, P., 498; 594 Maskell, S.J. See Fox, A.D., 60; 121 Maso, M. See Sabates, A., 285; 305 Mason, C.F., 528, 538, 566, 569; 595; See Samuels, A.J., 565; 601 Mason, D.C., 85; 129 Mason, D.W. See Holler, N.R., 589 Massei, G., 560; 595 Masuhara, H. See See Nakano, Y., 303 Masuzawa, T. See Kitazato, H., 226 Matamoros, M.C. See Campos, E., 579 Matano, R.P. See Palma, E.D., 46, 87; 132 Matear, R.J. See Wong, C.S., 232 Mathers, E.L., 24; 31 Mathieu, P.P. See Beckers, J.-M., 113 Mathieu, P.-P., 62; 129 Mathot, S. See DiTullio, G., 162; See Garrison, D.L., 144, 156; 163; See Lancelot, C., 127 Matishov, G.G. See Forman, S.L., 586 Matlick, H.A. See Prézelin, B., 167 Matovelo, J.A. See Ngomuo, A.J., 597 Matsubara, N. See Senjyu, T., 33 Matsuda, Y. See Lim, C.P., 418 Matsumasa, M. See Takeda, S., 604 Matsumoto, J. See Powell, G.L., 236; 304 Matsumoto, K., 24; 31 Matsuoka, H. See Yoshida, S., 608 Matsutomi, H. See Tsuji, Y., 605 Matsuyama, K. See Yano, K., 425 Matsuyama, M. See Senjyu, T., 33; See Tsuji, Y., 605

643

AUTHOR I ND E X

Matthaei, C.D., 280; 302 Matthews, K., 84; 129 Mattila, J., 434, 438, 473; 484; See Bonsdorff, E., 479; See Jumppanen, K., 436, 438, 473; 483; See Kraufvelin, P., 483 Mattson, J., 52, 68; 129 Matyushkin, E.N., 573; 595 Mauchamp, A., 493, 494; 595; See Mesleard, F., 596 Mauri, E. See Crise, A., 117; See Crispi, G., 117 Mawson, P.R., 527, 553; 595 May, R.M., 314, 317, 318, 319; 338 Maybaum, H.L. See Bennett, B.A., 335; See Smith, C.R., 340 Mayer, B. See Dick, S., 119; See Pohlmann, T., 133; See Puls, W., 134 Mayer, L.M. See Jumars, P.A., 226, 338 Mayes, R.W. See Howard, B.J., 590 Mazzega, P. See Houry, S., 26; 30 McAlpine, D.F., 536; 595 McCabe, T.R. See Walsh, N.E., 606 McCanch, N., 511; 595 McCann, L.D. See Levin, L.A., 338 McCann, T.S. See Fedak, M.A., 585 McCarthy, J.J. See Robinson, A.R., 229 McCave, I.N. See Green, M.O., 83; 122; See Hollister, C.D., 313; 337; See Jago, C.F., 226 McChesney, G.J., 494, 522, 536; 595 McClanahan, T.R., 397, 399; 418 McClatchie, S., 326; 338 McClimans, T.A., 21; 31; 47, 51, 54; 129 McClintock, J.B. See Watts, S.A., 424 McCluskie, A., 540; 595 McComb, A.J., 243, 255; 302; See Cambridge, M.L., 263; 295; See Gordon, D.M., 297; See Larkum, A.W.D., 300; See Lukatelich, R.J., 270, 271; 302; See Silberstein, K., 306 McConkey, S. See Bradshaw, C.J.A., 579 McConnell, B.J. See Fedak, M.A., 585 McCook, L.J., 269; 302; See Umar, M.J., 307 McCord, T.B., 144; 166 McCracken, D.I., 511, 573; 595 McCulloch, M., 253; 302 McCulloch, M.T. See Alibert, C., 253; 294 McCutcheon, S.N. See Parker, W.J., 557; 598 McDonagh, T.J., 492; 595 McDonald, P. See Clifton, J., 116 McGarry, C. See Spencer, R., 34 McGillicuddy, D.J. See Robinson, A.R., 229 McGowan, J.A., 325; 338

McGrady, M.J., 546; 595 McInnes, K.L. See Hubbert, G.D., 85; 124 McIvor, C.C., 287; 302 McKay, W.A., 568; 596 McKenzie, C. See Boon, J.P., 578 McKenzie, L.J. See Udy, J.W., 307 McKinnon, A.D. See Thorrold, S.R., 307 McLachlan, A. See Ascaray, C.M., 576; See Kerley, G.I.H., 592 McLean, J.G., 366; 418 McLellan, W.A. See Scholin, C.A., 602 McLusky, D.S., 280; 302 McMahon, T.A. See Finlayson, B.L., 238, 239; 297; See Maheshwari, B.L., 302 McManus, J.P., 73, 93; 129, 130; See Baumert, H., 113 McManus, J. See Prandle, D., 134 McMaster, R.L., 508; 596 McMeekin, T.A. See Nichols, D.S., 166 McMinn, A., 148, 149, 152, 159, 160, 161; 166; See Gibson, J.A.E., 163; See Trenerry, L.J., 169 McNaught, D.C., 398; 418, 419 McPeak, R.H. See Barilotti, D.C., 410 McPhaden, M.J., 4; 31 McPherson, B.F., 276; 302 McRae, J. See Richmond, N.T., 421 McShane, P.E., 383, 384, 398; 419 McWilliams, J.C. See Gent, P.R., 73, 89; 121; See Large, W.G., 127; See Marchesiello, P., 87; 129 Meade, L.D. See Moore, D.S., 419 Meador, J.P. See Varanasi, U., 606 Meehan, A.J., 555; 596 Meek, P.D., 529, 572; 596 Meelis, E. See Deiongh, H.H., 582 Meeter, D.A., 276, 278; 302 Mehra, A. See Chassignet, E.P., 116 Meier, H.E.M., 45, 51, 81; 130 Meier, M.F. See Warrick, R.A., 34 Meincke, J., 44, 130; See Dickson, B., 29 Meinesz, A. See Caye, G., 257, 258; 295 Meininger, P.L. See Goss-Custard, J.D., 587 Melillo, J.M. See Vitousek, P.M., 606 Mellor, G.L., 54, 75, 102; 130; See Blumberg, A.F., 47; 114; See Ezer, T., 29 Melnikov, I.A. See Horner, R., 164; See Legendre, L., 165; See Schnack-Schiel, S.B., 168 Melsom, A. See Martinsen, E.A., 46; 129 Melville, M.D. See Sammut, J., 305

644

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Mémery, L. See Lévy, M., 128 Ménard, Y. See Haines, B.J., 18; 30 Ménard, Y. See Mitchum, G.T., 32 Mendéz, F.J., 102; 130 Ménesguen, A., 97; 130 Menesguen, A. See Guillaud, J.-F., 122; See Le Pape, O., 44, 97; 127 Meñez, L.A. See Juinio, M.A., 416 Meredith, M.P., 19, 20; 31; See Hughes, C.W., 30; See Woodworth, P.L., 35 Merriﬁeld, M., 21; 32; See Johnston, T.M.S., 22; 30; See Kilonsky, B., 31; See Kilonsky, B.J. 31; See Woodworth, P.L., 1–35 Mescharov, S.L. See Shapiro, G.I., 136 Mescher, M.C. See Fong, P., 297 Mesinger, F., 59; 130 Mesleard, F., 505; 596 Messeri, P., 563; 596 Messier, F. See Ferguson, S.H., 505 Metaxas, A., 273; 302 Metwally, H. See Abouheif, M.A., 575 Metzner, M., 60, 85; 130 Meyer, A. See Kretzmann, M.B., 592 Meyer, M.A. See Oosthuizen, W.H., 598 Meyers, G. See McPhaden, M.J., 31 Michaux, J. See Boye, P., 578 Michel, C. See Guillou, M., 390, 405; 415 Michener, W.K. See Gardner, L.R., 586 Micklin, P.P., 235; 302 Micol, T., 494, 495; 596 Middelburg, J.J. See Moodley, L., 228 Mignucci-Giannoni, A.A., 527, 574; 596 Miksis, J.L. See Connor, R.C., 580 Milanelo, M. See Martuscelli, P., 595 Militeev, A.N. See Sladkevich, M., 137 Millar, R.B. See McClatchie, S., 338 Miller, A.P., 525; 596 Miller, B.K., 500; 596 Miller, D. See Thompson, P., 605 Miller, D.C. See Bernard, F.R., 386, 403, 405; 411 Miller, F.L., 573; 596 Miller, G.H. See Forman, S.L., 586 Miller, K. See Roughgarden, J., 421 Miller, K.V. See Gassett, J.W., 586 Miller, M. See Morton, J., 492; 596 Miller, M.W., 266, 268; 303 Miller, P.E. See Scholin, C.A., 602 Miller, R.J., 216, 217; 228; 381, 382, 407; 419; See Andrew N.L., 343– 425; See Moore, D.S., 419

Miller, S.L. See Miller, M.W., 303 Millero, F.J. See Gleitz, M., 164 Millet, B. See Pinazo, C., 133 Millie, D.F. See Wear, D.J., 308 Millien-Parra, V., 495; 596 Mills, E.L., 312; 338 Mills, J.P. See Twiss, S.D., 605 Mills, K. See Knighton, A.D., 592 Millward, G.E. See Liu, Y.P., 128; See Tappin, A.D., 138; See Jago, C.F., 226 Milne, H. See Raffaelli, D.G., 600 Minas, H. See Denis-Karaﬁstan, A., 119 Mineau, P. See Howald, G.R., 590 Mingham, C.G., 58; 130; See Hu, K., 124 Minh, T.B., 567; 596 Ministry of Maritime Affairs and Fisheries, 378, 379; 419 Minster, J.-F. See Fu, L.-L., 29 Mirza, F.B., 467; 484 Misic, C., 157; 166 Miskelly, C.M. See Empson, R.A., 523; 584 Mitchell, B.G. See Wassmann, P., 232 Mitchell, C., 149, 150; 166 Mitchell, D. See Harris, P.T., 298 Mitchell, J.L. See Jacobs, G.A., 20; 30 Mitchell, L., 177; 228 Mitchell, R., 276; 303 Mitchell-Innes, B. See Kiørboe, T., 226 Mitchum, G., 12; 32 Mitchum, G.T., 15, 17, 25, 26; 32; See McPhaden, M.J., 31; See Nerem, R.S., 17, 22; 32; See Potemra, J.T., 33; See Ray, R.D., 25; 33; See Woodworth, P.L., 1–35 Miyaji, M. See Suzaki, A., 604 Miyamoto, B. See Mitchum, G., 32 Miyamoto, T. See Agatsuma, Y., 410 Miyazaki, N. See Minh, T.B., 596 Mizdalski, E. See Thomas, D.N., 168 Mladenov, P.V. See Scheibling, R.E., 392; 422 Mock, T., 153, 159; 166; See Krembs, C., 165 Modig, H. See Olafsson, E., 228 Moeller, P.D.R. See Scholin, C.A., 602 Moffat, T.J. See Dyer, K.R., 73; 120; See Howarth, M.J., 124 Mohan, R.K., 92; 130 Møhlenberg, F. See Conley, D.J., 480 Molcard, R., 21; 32 Molenaar, M. See Wahr, J., 34 Moline, M.A. See Prézelin, B., 167 Molines, J.M. See Le Provost, C., 31; 128

645

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Molines, J.-M. See Barnier, B., 113; See Shum, C.K., 33 Moll, A., 38, 44, 49, 51, 95; 130; See Pohlmann, T., 133; See Skogen, M.D., 95; 137 Moller, H., 519, 520, 571; 596; See Ragg, J.R., 546; 600 Moller, J.S. See Jensen, H.R., 125 Möller, I., 102; 130 Möller, P. See Rosenberg, R., 487 Molles, M.C., 240; 303 Molls, F. See Molls, T., 55; 130 Molls, T., 55; 130 Mölsä, H., 472, 475; 484 Monaco, M.E. See Livingston, R.J., 301 Monaghan, P. See Furness, R.W., 523, 530, 536; 586 Monbaliu, J., 49, 51, 99; 130; See Berlamont, J., 114; See Ozer, J., 132 Moncrieff, C.B. See Lambshead, P.J.D., 338 Mondadori, A.J. See Gava, A., 587 Mondon, K. See Sanchez, A.L., 602 Monismith, S.G. See Jassby, A.D., 299; See Lucas, L.V., 301 Monk, J.D. See Garvine, R.W., 249; 297 Montague, C.L., 256; 303 Montecinos, M.A., 353; 419 Monteiro, L.R., 494; 596; See Furness, R.W., 586 Montero, P. See Gomez-Gesteira, M., 122; See Taboada, J.J., 138 Montevecchi, W.A. See Sklepkovych, B.O., 532; 603 Montoya-Ospina, R.A. See Mignucci-Giannoni, A.A., 596 Montresor, M., 144; 166 Moodley, L., 212, 213, 214, 215; 228 Mooers, C.N.K., 47, 51, 66; 131; See Deleersnijder, E., 119 Mooney, H.A. See Vitousek, P.M., 606 Moore, A.D. See Wear, D.J., 308 Moore, D. See Galindo-Bect, M.S., 297 Moore, D.S., 381; 419 Moore, G.F. See Hall, S.J.G., 492, 559; 588 Moore, K.A., 259, 262; 303 Moore, M.J. See Weisbrod, A.V., 607 Moore, P., 17; 32; See Murphy, C.M., 32 Moore, P.G., 491– 608; 493; 596 Moore, S. See Smetacek, V., 168 Moore, W.S., 81; 131; See Lauerman, L.M.L., 227 Moorhouse, A. See Kruuk, H., 538, 539; 592

Moors, P.J., 493, 536; 596 Morales-Vela, B., 554; 596; See Axis-Arroyo, J., 576 Morelli, L. See Diguardo, G., 582 Moreno, C.A., 349, 352, 406; 419; See Andrew N.L., 343– 425; See Castilla, J.C., 349, 397; 412; See Gebauer, P., 349; 414; See Zuleta, A., 349, 406; 425 Moreno, L. See Nicholson, J., 131 Morera, G. See Lozano, J., 418 Morgan, L. See Gulland, F.M.D., 588 Morgan, L.E., 370, 404, 405, 406; 419; See Botsford, L.W., 411; See Gerber, J.A., 587; See Hanni, K.D., 588; See Lundquist, C.J., 418 Morikawa, T., 396; 419 Morley, A.W. See Brown, T.E., 295 Moro, D., 572; 596 Moro, I. See Andreoli, C., 162 Morris, A.W. See Harris J.R.W. 123; See Jago, C.F., 226 Morris, G. Surey-Gent, S., 510; 604 Morris, K. See Moro, D., 572; 596 Morris, P.A. See Worthy, G.A.J., 607 Morris, R.D. See Burness, G.P., 579 Morris, R.J. See Rice, A.L., 229, 339 Morrisey, D.J., 280; 303 Morse-Porteous, L.S. See Grassle, J.F., 179; 225; 316, 320, 324, 328, 330, 331, 332; 337 Mortier, L. See Tusseau-Vuillemin, M.-H., 139 Mortimer, G. See McCulloch, M., 302 Morton, J., 492; 596 Morton, J.E., 496; 597 Mory, M. See Péchon, P., 132 Moseid, K.E. See Heggberget, T.M., 540; 589 Mosher, S. See Banse, K., 471; 479 Motoya, S. See Agatsuma, Y., 410 Moubahser, A.H., 556; 597 Mountfort, G. See Peterson, R., 599 Moy, F. See Berge, J.A., 479 Moyer, C. See Giovannoni, S., 336 Moylan, E., 391, 392, 396; 419 Mueller, P.J. See Jumars, P.A., 226 Muir, D.C.G. See Kuhnlein, H.V., 593; See Norstrom, R.J., 567; 598 Mulicki, Z., 449; 484; See Demel, K., 448, 449, 450; 480 Muller, R. See Childers, D., 295 Müller, A. See Puls, W., 134 Muller-Karger, F. See Thunell, R.C., 231 Mullineaux, L. See Wishner, K., 232

646

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Mulrennan, M.E., 570; 596 Munch-Petersen, S. See Bagge, O., 461; 479 Munoz, A.A. See Ojeda, F.P., 492; 598 Muntz, L., 406; 419 Murai, M. See Takeda, S., 604 Murariu, D. See Krystufek, B., 593 Murdock, R.N. See Jones, S.R., 591 Murphy, C.M., 17, 27; 32 Murphy, D. See Pohlmann, T., 133; See Puls, W., 134 Murphy, E.C. See Dowding, J.E., 493; 583 Murray, D. See Walsh, I., 232 Murray, J., 312; 338 Murray, J.W. See Oguz, T., 131 Murray, L. See Boynton, W.R., 294 Murray, S.P. See Arief, D., 21; 28 Murray, W.H., 538; 597 Murty, T.S. See Singh, O.P., 33 Mutzke, A. See Fennel, W., 55; 121 Mwamengele, G.L.M. See Ngomuo, A.J., 597 Myers, A.A. See Kiely, O., 492, 548; 592 Myers, K., 500, 521, 523; 597 Myers, P.G., 46, 80; 131; See Samuel, S., 135 Myers, R.A., 402; 419; See Drinkwater, K.F., 277, 282; 296 Myklestad, S. See Kristiansen, S., 165 Myrberg, K. See Alenius, P., 478 Mysak, L.A. See Holland, D.M., 123 Myslivets, V. See Forman, S.L., 586 Nacken, N., 492; 597 Naeem, S. See Costanza, R., 295 Nagorsen, D.W. See Bertram, D.F., 577 Nagy, J.A. See Larter, N.C., 566; 593 Naidoo, G., 255; 303 Naiman, R.J. See Palmer, M.A., 339 Naimie, C.E. See Lynch, D.R., 128 Nairn, R. See Cabot, D., 579 Nakahara, S. See Merriﬁeld, M., 32 Nakamura, T., 355, 396; 419 Nakano, Y., 269; 303 Nakao, S. See Agatsuma, Y., 410 Nakaoka, M., 555; 597 Nakata, A. See Yano, K., 425 Nakata, H. See Minh, T.B., 596 Nakatsuka, T. See Kitazato, H., 226 Nakonieczny, J. See Renk, H., 486 Napolitano, E., 98; 131 Narayanaswamy, B.E. See Bett, B.J., 222 Narins, P.M., 517; 597 Nash, L.A. See Ng, B., 131

Nassar, J. See Ceballos, G., 580 National Fisheries Research and Development Institute, 394, 407; 420 National Research Council, 540, 541; 597 Natynczuk, S.E. See Smith, P.A., 603 Naudin, J.J. See Durand, N., 120 Naudin, J.-J. See Maidana, A.M., 129 Navarette, S.A., 493, 510, 524, 525; 597 Nazarenko, L., 90; 131 Nealson, K.H. See Gaidos, E.J., 163 Neaverson, E., 188; 228 Neckles, H.A., 267; 303 Nédélec, H. See Boudouresque, C.F., 411 Nedwell, D.B., 151, 152, 161; 166; See Reay, D.S., 167 Nehring, D., 458; 484 Neilan, R., 25; 32 Neilson, C.G. See Gorman, R.M., 100; 122 Nekrasov, A. See Alenius, P., 478 Nekrasov, A.V. See Androsov, A.A., 112 Nel, J.A.J., 533; 597; See Avenant, N.L., 576; See Cavallini, P., 535; 580; See Louw, C.J., 535; 594 Nellen, W. See Thiel, R., 307 Nellis, D.W., 533, 534; 597 Nelson, D.M., 152; 166; See Smith Jr, W.O., 205; 231 Nelson, E. See Angerbjorn, A., 575 Nelson, L.A. See Harris, J.R.W., 123 Nelson, W.G. See Tunberg, B.G., 465; 488 Nemazie, D.A. See Purcell, J.E., 304 Nemoto, M., 559; 597 Nerem, R.S., 17, 22; 32; See Tapley, B.D., 34 Neshyba, S. See Clément, A., 412 Nettleship, D.N. See Birkhead, T.R., 531; 578 Neuhaus, R., 501; 597 Neumann, T. See Fennel, W., 97; 121 Neves, D.S. See Gava, A., 587 Neves, H.O. See Zino, F., 608 Neves, R. See Gomez-Gesteira, M., 122; See Martins, F., 129 Neveux, J. See Riaux-Gobin, C., 229 Newberger, P.A., 53; 131 Newell, S.Y. See Barlocher, F., 503; 576 Newman, J. See Beckmen, K.B., 577 Newman, K.A. See Stolzenbach, K.D., 231 Newton, P.P., 177, 187, 204; 228; See Lampitt, R.S., 227 Newton, S. See Cabot, D., 579 Nezlin, N.P. See Oguz, T., 131 Ng, B., 64; 131

647

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Ng, P.K.L., 513, 519, 542; 597 Ngomuo, A.J., 503; 597 Nhuan, M.T. See Church, J.A., 29 Nichols, D.S., 144, 159, 161; 166 Nichols, F.H., 276; 303 Nichols, P.D., 159; 166; See Gibson, J.A.E., 163; See Nichols, D.S., 166 Nicholson, J., 44, 51, 92; 131 Nicol, E.A.T., 522; 597 Nicolaidou, A. See Kormas, K.A., 226 Nielsen, S.P., 66, 73; 131 Niemistö, L. See Perttilä, P., 486 Nienhuis, P.H., 501; 597 Nietschmann, B. See Smethurst, D., 574; 603 Nieuwland, G. See Duineveld, G.C.A., 224 Nightingale, N. See Salter, R.E., 601 Nihoul, J., See Denis-Karaﬁstan, A., 119 Nihoul, J.C.J., 108; 131; See Beckers, J.-M., 113; See Delhez, E.J.M., 119; See Grégoire, M., 122 Niiler, P.P. See McPhaden, M.J., 31 Nijman, V., 564; 597 Nikolow, V. See Stanev, E.V., 69; 137 Nikulina, V.N. See Telesh, I.V., 488 Nilsen, J.H. See McClimans, T.A., 129 Nilssen, A.C. See Kearney, M.S., 591 Nilsson, C. See Jansson, R., 299 Nilsson, H.C., 462, 464, 465, 466; 484; See Rosenberg, R., 487 Nilsson, K. See Rosenberg, R., 487 Nishihira, M. See Sakai, K., 269; 305 Nissling, A., 477; 485 Nittis, K., 90, 107; 131; See Soukissian, O., 137 Nitz, T. See Dick, S., 119 Niu, X.-F. See Livingston, R.J., 301 NIVA, 464, 468; 485 Nival, P. See Lacroix, G., 98; 127 NOAA, 16; 32 Nöhren, I., 51; 131 Noji, T. See Riebesell, U., 229 Nolan, S.C. See Miller, R.J., 381, 407; 419 Nolet, B.A., 538, 540; 597 Nõmmann, S. See Pavelson, J., 132 Norcross, B.L., 279; 303 Nordby, E. See Kaartvedt, S., 275; 299 Norderhaug, M., 531; 597 Norheim, G. See Prestrud, P., 600 Norkko, A., 428, 437; 485; See Bonsdorff, E., 479; See Norkko, J., 485 Norkko, J., 437; 485 Norris, K., 571, 573; 597

Norstrom, R.J., 567; 598 Nøst, E., 68, 97; 131 Nothangel, J. See Kirst, G.O., 165 Nöthig, E.-M. See Thomas, D.N., 168 Novaczek, I., 265; 303 Novotny, M. See Gassett, J.W., 586 Nowell, A.R.M. See Yager, P.L., 232 Noye, B.J. See Arnold, R.J., 56; 112 Noye, J. See Matthews, K., 129 NRC, 20; 32 Numachi, Y. See Yoshida, S., 608 Nummelin, C., 435, 437; 485 Nunes, R.A. See Simpson, J.H., 249; 306 Nuzzio, D.B. See Luther, G.W., 227 Nyan Taw, 275; 303 Nybakken, J.W. See Oliver, J.S., 339 Nyberg, L. See Gustafsson, N., 122; See Omstedt, A., 47, 59, 82, 89, 110; 132 Nycander, J. See Meier, H.E.M., 130 Oberhuber, J.M. See Holland, D.M., 123; See Kauker, F., 126 Ocean Studies Board, 221; 228 Ochocki S. See Renk, H., 486 O’Connell, M. See Bonesi, L., 578 O’Connor, B. See Péchon, P., 132 O’Connor, B.A., 93; 131; See MacDonald, N.J., 93, 102, 110; 129; See Williams, J.J., O’Connor, M., 507; 598 O’Connor, P. See Lunney, D., 594 O’Connor, W.P., 22; 32 Odell, D.K. See Watanabe, M., 606 O’Donnell, G. See Tangney, D., 604 O’Donohue, M.J. See Longstaff, B.J., 301 Odum, E.P., 172; 228 Oebius, H. See O’Connor, B.A., 131 Oerlemans, J. See Warrick, R.A., 34 Oeschger, R., 472, 475; 485 Oey, L.-Y. See Kourafalou, V.H., 126 Ogilvie, S.C., 523; 598 Oguz, T., 67, 80, 86, 98, 99; 131, 132; See Napolitano, E., 131 Ohga, T., 183, 209; 228 Ohldag, S., 477; 485 Ojaveer, E., 442; 485 Ojaveer, H., 442; 485 Ojeda, F.P., 398; 420; 492; 598 Okada, Y. See Kitazato, H., 226 Okayasu, A. See Deigaard, R., 118 Olafsson, E., 214; 228 Olave, S. See Bustos, E., 412

648

AUTHOR I ND E X

Olbers, D.J. See Rintoul, S.R., 33 Oldﬁeld, F. See Clifton, J., 116; See O’Connor, B.A., 131 Olenin, S., 431, 447, 450; 485; See Leppäkoski, E., 431; 484 Oli, M.K., 570; 598 Oliva, M. See Zegers, J., 425 Oliveira, A. See Fortunato, A.B., 47, 65, 67; 121 Oliveira, F.S.B.F., 43, 102; 132 Oliveira, P. See Zino, F., 608 Oliveira, S.M. See Martins, I., 302 Oliver, J.S., 320; 339 Olivera-Gomez, D. See Morales-Vela, B., 596 Olley, J. See Nichols, D.S., 166 Olmos, F. See Martuscelli, P., 595 Olsgard, F., 467; 485; See Gray, J.S., 337 Olsson, I. See Rosenberg, R., 487 Ölundh, E. See Rosenberg, R., 487 Omori, M. See Sano, M., 422 Omstedt, A., 47, 59, 66, 69, 82, 89, 110; 132; See Gustafsson, N., 122; See Westman, P., 489 O’Neill, A.L. See Andrew, N.L., 390; 410 O’Neil, J. See Koop, K., 300 O’Neill, J.G. See Wyer, M.D., 608 O’Neil, T. See Lynch, J.J., 594 O’Neill, R.V. See Costanza, R., 295 Onken, R., 43, 64; 132 Ooe, M. See Matsumoto, K., 31 Oosthuizen, W.H., 533; 598 Open University Course Team, 205; 228 Orenga, P. See Bosseur, F., 114 Orensanz, J.M., 409; 420 Orgeur, P. See Arnould, C., 575 O’Riain, J. See Narins, P.M., 597 Oring, L.W. See Alberico, J.A.R., 575 Oriol, L. See Riaux-Gobin, C., 229 Orlando, E.F. See Crain, D.A., 581 Orlic, M. See Vilibic, I., 102; 139 Ormond, R.F.G., 333; 339 Oro, D., 537; 598 Orpin, C.G., 559; See Greenwood, Y., 588 Ortega-Rubio, A. See Alvarez-Cardenas, S., 575 Orth, R.J. See Dyer, K.R., 236; 296; See Moore, K.A., 303; See Neckles, H.A., 303 Ortiz, E. See Mauchamp, A., 595 Osborn, D.A. See Gassett, J.W., 586 Osowiecki, A., 449, 450, 451, 468, 473; 485 Osterhaus, A.D.M.E. See Barrett, T., 576 Osterhus, S. See Dickson, B., 29

Ostermann, D. See Fischer, G., 163 Östman, M., 435, 436; 485 Ostrowski, M. See Laane, R.W.P.M., 126 O’Sullivan, E.N., 537; 598 Osuna, P. See Ozer, J., 132 Osunkoya, O.O. See Dick, T.M. 255; 296 Otsu, I. See Gutiérrez, J., 353; 415 Otte, M.J., 492; 598 Otten, J.H., 173, 195; 228 Ouillon, S. See Arnoux-Chiavassa, S., 112; See Durand, N., 120 Overdorff, D.J. See Lehman, S.M., 593 Overing, J. See Mignucci-Giannoni, A.A., 596 Ovsyanikov, N.G., 573; 598 Owen, F.J. See Bates, M.R., 577 Owen, M., 497, 530; 598 Oxenford, H.A. See Vermeer, L.A., 424 Ozanne, F., 53; 132 Ozer, J., 99; 132; See Jamart, B.M., 84; 124; See Luyten, P.J., 128; See Ruddick K.G., 135 Özkan-Haller, H.T., 85; 132 Ozmidov, R.V. See van Dam, G.C., 139 Pacala, S. See Tilman, D., 323, 324; 341 Pacanowski, R.C. See Pinardi, N., 133 Pachel, K. See Pitkänen, H., 486 Packham, J.R., 520; 598 Pacunski, R.E. See Bradbury, A., 411 Padilla-Hernández, R. See Monbaliu, J., 130; See Ozer, J., 132 Paerl, H.W. See Mallin, M.A., 302 Page, H.M. See Galindo-Bect, M.S., 297 Page, J.S. See Wong, C.S., 232 Pain, C.C., 44, 57; 132 Pain, D.J., 522, 523; 598 Paine, R.T., 493, 572; 598 Painter, E.L., 573; 598 Paiva, A.M. See Chassignet, E.P., 116 Palacín, C., 384, 385; 420 Palacín, C. See Lozano, J., 418; See Turon, X., 423 Palliero, J.S., 372, 373; 420 Palleiro, J.S. See Andrew, N.L., 343– 425 Palma, E.D., 46, 87; 132 Palmer, C., 513; 598 Palmer, M.A., 319; 339 Palmisano, A.C., 144, 156, 159; 166; See Nichols, P.D., 166; See Priscu, J.C., 167 Palmisano, A.W., 505; 598 Palmisano, J.F. See Estes, J.A., 365; 414, 584 Palsson, W.A. See Bradbury, A., 411

649

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Paluszkiewicz, T., 66; 132 Pan, S. See Williams, J.J., 140 Panayotidis, P. See Le Direac’h, J.P., 418 Panchaban, S. See Nemoto, M., 559; 597 Pancucci, A. See Le Direac’h, J.P., 418 Pank, L.F. See Walsh, N.E., 606 Panov, V.E. See Telesh, I.V., 488 Papdimitriou, S. See Mock, T., 169 Parer, I. See Myers, K., 597 Parﬁtt, S.A. See Bates, M.R., 577 Pariwono, J. See Bray, N.A., 28 Park, Y.-H. See Verstraete, J.-M., 15; 34 Parke, M. See Shum, C.K., 33 Parker, D. See Allan, R., 294 Parker, W.J., 557; 598 Parkes, J.P., 494; 560; 598; See Chimera, C., 580 Parkinson, C.L., 81, 86; 132 Parkkonen, L. See Andersin, A.-B., 478 Parsons, E.C.M., 527, 528; 598 Parsons, R. See Raven, J.A., 167 Parsons, T.R. See Brown, P.S., 172; 223 Paruelo, J. See Costanza, R., 295 Pass, D.M. See Eschler, B.M., 584 Passow, U., 194, 201; 228; See Alldredge, A.L., 222; See Riebesell, U., 229 Pastres, R. See Bergamasco, A., 114 Patching, J.W. See Thiel, H., 231, 341 Patel, T., 246; 303 Paterson, A.W. See Whitﬁeld, A.K., 570; 607 Paterson, G.L.J., 317, 326; 339; See CossonSarradin, N., 335; See Eckman, J.E., 336 Paterson, I.W., 558, 559, 560; 599; See Greenwood, Y., 588; See Orpin, C.G., 598 Paterson, S.M. See Killworth, P.D., 126 Paterson, S. See Pemberton, J.M., 599 Pätsch, J. See Laane, R.W.P.M., 126; See Smith, J.A., 137 Patten, D.T. See Wiens, J.A., 308 Patterson, I.J. See Raffaelli, D.G., 600 Pattiaratchi, C.B. See Harris, P.T., 298 Pavelson, J., 52; 132 Pavlis, D. See Tapley, B.D., 34 Pavlov, P. See Woodall, P.F., 607 Pawson, D. See Levin, L.A., 342 Payne-Gallwey, R., 566; 599 Pearce, C.M., 560; 599 Pearce, R.B. See Kemp, A.E.S., Pearse, J.S., 405; 420; See Lowry, L.F., 541; 594 Pearse, P.H. See Walters, C., 403; 424

Pearson, E.L. See Lundquist, C.J., 418 Pearson, T.H., 430, 437, 460, 463, 468, 472, 473, 476; 486; See Bonsdorff, E., 431, 473; 479; See Rosenberg, R., 487; See Rumohr, H., 487 Pecenik, G. See Bergamasco, A., 114 Péchon, P., 51, 102; 132 Peckham, S.H. See Wahle, R.A., 382, 405; 424 Peckol, P., 267, 268; 303 Peinert, R., 179, 187; 228 Pekol, P. See Valiela, I., 307 Peled, Y. See Kutiel, P., 593 Pelinovskii, E.N. See Talipova, T.G., 138 Pelinovsky, E., 53; 182; See Holloway, P.E., 123, 124; See Grimshaw, R., 122 Pelinovsky, E.N. See Ivanov, V.A.S., 124 Pemberton, I.J. See Gildersleeve, R.R., 587 Pemberton, J.M., 495; 599; See Coltman, D.W., 580 Pendrey, R. See Vance, D.J., 308 Penduff, T., 87; 132 Pengprecha, P. See Feldmann, J., 585 Pennington, J.T., 403, 404; 420; See Roughgarden, J., 421 Pennock, J.R. See Heck, K.L., 298 Pentony, M., 370; 420 Peregrine, D.H. See Donato, A.N., 119 Pérez, F.F. See Rosón, G., 135 Perez Gomez, B. See Alvarez Fanjul, E., 112 Pérez-Padilla, J. See Mignucci-Giannoni, A.A., 596 Perez-Villar, V. See Gomez-Gesteira, M., 122 Pérez-Villar, V. See Taboada, J.J., 138 Periáñez, R., 47, 73; 132 Perillo, G.M.E., 246; 303 Perrin, M.R. See Ascaray, C.M., 576; See Fielden, L.J., 585 Perron, R. See Sibuet, M., 230 Perry, C.J. See Abal, E.G., 293 Perry, R.I., 364, 365, 402, 403; 420; See Waddell, B.J., 424 Pers, C., 55; 133 Persson, G. See Rosenberg, R., 487 Persson, J., 428, 436, 446, 454; 486 Perttilä, P., 439; 486 Perus, J., 437; 486 Peterken, C.J., 555; 599 Peters, H. See Baumert, H., 75; 113 Peters, J. See Berry, R.J., 577 Petersen, C.G.J., 190; 228; 466; 486 Peterson, B.J., 200; 228

650

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Peterson, C.H., 326; 339; 493, 546, 560, 561; 599; See Botsford, L.W., 411 Peterson, O. See Burchard, H., 63, 76; 115 Peterson, R., 520; 599 Peterson, R.G. See Whitworth, T., 19; 35; See Woodworth, P.L., 35 Petrecca, R.F. See Snelgrove, P.V.R., 341 Petricig, R.O., 529; 599 Petterson, L.H. See Johannessen, O.M., 125 Petts, G., 234, 240; 303 Petty, R.L. See Galindo-Bect, M.S., 297 Peyret, R. See Ehrenstein, U., 56; 120 Pfannkuche, O., 172, 177, 183, 195, 203, 204, 206, 211, 212, 213, 215; 228, 229; See Gooday, A.J., 224; See Thiel, H., 231, 341; See Witte, U., 232 Pfeiffer, W.J., 501, 525; 599 Pﬁster, C.A., 386, 387, 405; 420 Pﬁzenmayer, A. See Langenberg, H., 127 Pham, M.K., 97; 133 Philippart, M. See Gerritsen, H., 122 Phillips, A.V. See Goldstein, T., 587 Phillips, J.H. See Holland, L.Z., 415 Phillips-Howard, K.D., 566; 599 Picaut, J., 15; 32 Picaut, J. See Fu, L.-L., 29; See McPhaden, M.J., 31 Pich, W.C., 508; 599 Pichevin, T., 52; 133 Pielou, E.C., 531, 532, 546, 547; 599 Pierce, R.J. See Ogilvie, S.C., 598 Pierini, S., 45, 80, 86; 133 Pierson, E.D. See Elmquist, T., 584 Piertney, S.B. See Telfer, S., 604 Pietrzak, J.D. See Kliem, N., 48, 63; 126 Pietrzak, J. See Rasmussen, E., 134; See McClimans, T.A., 129 Pigozzi, G., 538; 599 Pihl, L., 462; 486; See Baden, S.P., 478, 479; See Breitburg, D.L., 479 Pike, J. See Kemp, A.E.S., 226; See Pilskaln, C.H., 181, 202, 207, 209, 219; 229 Pike, S.E. See Malone, T.C., 302 Pilkington, J.G. See Coltman, D.W., 580 Pilskaln, C.H., 181, 190, 201, 202, 207, 209, 219; 229; See Beier, J.A., 222 Pimm, S.L., 494; 599 Pinardi, N., 66, 80; 133; See Castellari, S., 116; See Roussenov, V., 135; See Young, E.F., 141 Pinazo, C., 45, 86, 94; 133 Pinckney, J.L. See Dustan, P., 271; 296

Pineda, J. See Levin, L.A., 342 Pinot, J.-M., 66; 133; See Ardhuin, F., 112 Pinturier-Geiss, L., 185, 200; 229 Pisarevskaya, L.G. See Bertram, V., 114 Pitcher, K.W., 400; 420 Pitkänen, H., 439, 440, 441, 442, 476; 486; See Gran, V., 439, 442; 481; See Leppänen, J.-M., 484 Pitt, K.A. See Kingsford, M.J., 300 Plag, H.-P., 23; 32 Plaia, G. See Blair, N.E., 222; See Levin, L., 227; See Levin, L.A., 227 Plater, A. See Clifton, J., 116 Platt, U. See Wagner, T., 151; 169 Player, R. See Prandle, D., 134 Plaza, F. See Lafuente, J.G., 127 Plotz, J. See Bornemann, H., 578 Plumstead, E.E., 284; 303 Podewski, S. See Robinson, A.R., 229 Pohlmann, T., 45, 66, 74, 75, 77, 78, 79, 81, 86, 88, 90, 97; 133; See Becker, G.A., 113; See Dick, S., 119; See Lenhart, H.-J., 74; 127; See Luff, R., 74; 128; See Puls, W., 134 Polis, G.A. See Rose, M.D., 498, 535; 601 Pollard, D.A., 250, 254, 255, 281, 282; 303, 304 Pollard, R.T. See Challenor, P.G., 28 Pollock, W.H. See Sturges, W.T., 168 Polloni, P.T. See Rowe, G.T., 340 Polovina, J.J., 365; 420 Polunin, N.V.C. See Dalzell, P., 413 Polyokov, I.V., 82; 133 Pomeroy, L.R., 151, 157; 161 Pomeroy, P.P., 510, 548; 599; See Twiss, S.D., 605 Pomroy, A.J. See Howarth, M.J., 124 Ponat, A. See Theede, H., 488 Poncet, A. See Le Provost, C., 46; 128 Ponchaut, F., 25; 32 Ponchaut, F. See Cazenave, A., 28 Pond, S. See Large, W.G., 79; 127 Pont, D. See Ibanez, C., 590 Ponte, R.M., 22; 32, 33; See Gaspar, P., 22; 29 Poore, G.C.B., 315, 316, 317, 319, 321, 322, 324, 326; 339; See Gray, J.S., 337 Papadimitrious, S. See Mock, T., 166 Popay, I., 504; 599 Pope, L.C. See Eldridge, M.D.B., 584 Pope, R. See Schaff, T., 340 Pope, R.H. See Smith, C.R., 230, 340 Popov, I.U. See Spaans, B., 603 Popova, E.E. See Lampitt, R.S., 227

651

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Poremba, K. See Dick, S., 119 Porter, D.L., 15; 33; See Robinson, A.R., 229 Porter, J.W., 269; 304 Porter, K.G. See Porter, J.W., 304 Postel, S.L., 234, 245, 287; 304 Potapov, E. See McGrady, M.J., 595 Potemra, J.T., 21; 33 Potter, I.C., 243, 282, 283, 284; 304; See Gill, H.S., 282, 283; 297; See Lenanton, R.C.J., 282; 301; See Loneragan, N.R., 283; 301; See Young, G.C., 309 Poulin, M. See Riaux-Gobin, C., 167 Povero, P. See Misic, C., 166 Povilitis, A., 570; 599 Povinec, P. See aetina, M., 116 Powell, C.L. See Scholin, C.A., 602 Powell, G.L., 236; 304 Powell, G.V.N., 256, 260, 263; 304; See Smith, T.J., 306 Powell, J.A. See Lefebvre, L.W., 554; 593 Powell, T.M. See Jassby, A.D., 299 Powilleit, M., 451, 452, 468; 486 Powilleit, M. See Kube, J., 474; 483 Pozdnyakov, D.V., 98; 133 Pradel, R. See Oro, D., 537; 598 Prahl, F.G. See Jumars, P.A., 226 Prandle, D., 37, 73, 83, 91, 105, 107; 133, 134; See Berlamont, J., 114; See Lane, A., 67; 127; See McManus, J.P., 73, 93; 129, 130; See Wolanski, E., 140; See Wolf, J., 101; 140 Prasad, S.N., 512; 599 Prat, N. See Ibàñez, C., 124 Prater, A.J., 492; 599 Preece, R.C. See Bates, M.R., 577 Preen, A., 493, 554, 555, 570; 599 Preen, A.R., 262, 286; 304 Prego, R., See Taboada, J.J., 138; See GomezGesteira, M., 122 Prell, W. See Honjo, S., 225 Prestrud, P., 567, 600 Prézelin, B., 149; 167 Price, D. See Nicholson, J., 131 Price, I.R. See Umar, M.J., 307 Price, M.V. See Chase, M.K., 580 Priddel, D., 523; 600; See Lunney, D., 594 Priddle, J. See Reay, D.S., 167 Prieur, L. See Leveau, M., 306 Prime, J.H. See Anderson, S.S., 575 Prince, J.S., 382; 420 Prins, H.H.T. See Bakker, J.P., 576 Priscu, J.C., 154, 155, 159; 167

Priscu, L.R. See Priscu, J.C., 167 Probert, R.J. See Brenchley, J.L., 257, 258; 295 Procaccini, G. See Montresor, M., 166 Proctor, R., 47, 48, 72, 86, 89, 92, 97, 102, 104, 106; 134; See Allen, J.I., 111; See Howarth, M.J., 124; See Hydes, D.J., 124; See Jones, J.E., 125; See Tartinville, B., 138; See Tsimplis, M.N., 139; See Xing, J., 140 Provost, C., See Vigan, X., 139 Prudente, M.S. See Minh, T.B., 596 Puckridge, J.T., 236, 239; 304; See Kingsford, R.T., 300 Puddister, C. See Silva, M., 602 Pugh, P.J.A., 508; 600 Pugsley, C.W. See Villouta, E., 424 Puhakka, M. See Mölsä, H., 484 Puls, W., 92, 103; 134; See Pohlmann, T., 133 Purcell, J.E., 280; 304 Purdie, D.A. See Howarth, M.J., 124 Pusceddu, A., 159; 167; See Guglielmo, L., 164 Pusey, B.J. See Arthington, A.H., 236; 294 Pustelnikov, O., 450; 486 Putney, B.H. See Tapley, B.D., 34 Puttick, G.M., 492, 572; 600 Pyle, P. See Hanni, K.D., 588 Qin, D. See Church, J.A., 29 Quammen, D., 497, 567; 600 Quattro, J.M. See Chase, M.R., 335 Queisser, W. See Graf, G., 225 Quillfeldt, C.H. See Kristiansen, S., 165 Quinlan, S.E., 530, 540; 600 Quinn, J.F., 369, 370, 405; 421; See Botsford, L.W., 411; See Morgan, L.E., 419; See Wing, S.R., 425 Quinn, N.W., 282; 304 Quinn, T.P., 250; 304; 546, 547; 600 Raabe, T. See Pohlmann, T., 133 Rabalais, N.N., 253, 284; 304 Rabalais, N.N. See Justic, D., 299 Rabilloud, F. See Le Provost, C., 31; 128 Rabinovich, A.B. See Metzner, M., 130 Rabouille, C. See Gehlen, M., 224 Rachev, N.H. See Stanev, E.V., 137; See Staneva, J.V., 138 Radach, G. See Berlamont, J., 114; See Laane, R.W.P.M., 126; See Moll, A., 38, 49, 51; 130 Radford, P.J. See Allen, J.I., 111; See Harris J.R.W., 123 Raffaelli, D.G., 566; 600

652

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Raga, J.A. See Aznar, F.J., 576 Ragen, T.J. See Craig, M.P., 549; 581 Ragg, J.R., 546; 600 Raghunath, R. See Fukumori, I., 29 Ragueneau, O. See Lampitt, R.S., 227 Rahm, L. See Pers, C., 55; 133; See Wulff, F., 489 Rahman, M.S. See Singh, O.P., 33 Rainey, W.E. See Elmquist, T., 584 Rajar, R. See aetina, M., 116 Rakha, K.A., 53; 134 Ralph, N. See Conte, M.H., 223 Ralph, P.J., 245; 304 Ramdohr, S. See Bornemann, H., 578 Ramos, J.A. See Monteiro, L.R., 596 Ramsey, D.S.L., 515, 570; 600 Ramsing, N.B. See Snelgrove, P.V.R., 341 Randall, J.E. See Seaman, G.A., 554; 602 Randall, R.E., 509; 600; See Fuller, R.M., 509; 586 Rankin, J.V. See Schroeter, S.C., 422 Ranwell, D.S., 492, 493, 501, 502, 503, 505, 506, 519, 520, 535, 556, 565; 600 Raper, S.C.B., 22; 33 Rapp, R.H. See Harangozo, S.A., 30 Raskin, R.G. See Costanza, R., 295 Rasmussen, B. See Conley, D.J., 480; See Jensen, H.R., 125; See Josefson, A.B., 458, 462; 482 Rasmussen, E., 55; 134 Rasmussen, E.K. See Baretta-Bekker, J.G., 113 Rathbun, G.B. See Morales-Vela, B., 596 Rathburn, A.E. See Gooday, A.J., 224 Ratnaswamy, M.J., 493, 535, 565, 600 Rattnansen, C. See Fonselius, S.H., 446; 481 Ratz, H. See Moller, H., 596 Rau, G.H., 160; 167 Raven, J.A., 149, 150, 152, 154; 167 Raventos, H. See Campos, E., 579 Raviraja, N.S. See Arun, A.B., 576 Ray, G.C., 318; 339 Ray, G.L. See Livingston, R.J., 301 Ray, R., 24; 33; See Shum, C.K., 33 Ray, R.D. 23, 25; 33; See Cartwright, D.E., 23; 28; See Woodworth, P.L., 35 Raymo, M.E. See Cronin, T.M., 314, 325, 334; 335 Raymond, J.A., 158; 167 Rayner, M.S. See Nichols, P.D., 166 Read, J.F. See Challenor, P.G., 28 Reay, D.S., 152, 154, 155; 167

Receveur, O. See Chan, H.M., 567; 580; See Kuhnlein, H.V., 593 Reeburgh, W.S. See Horner, R., 164; See Legendre, L., 165 Reed, J.M. See Alberico, J.A.R., 575 Reeh, N. See Raper, S.C.B., 33 Regener, M. See Baumert, H., 113 Reidman, M.L., 400; 421 Reigstad, M. See Riebesell, U., 229 Reimchen, T.E., 492, 547; 600; See Byun, S.A., 579 Reimer, A. See Puls, W., 134 Reimers, C.E., 183, 195, 199, 201, 202, 208, 209; 229; See Jumars, P.A., 226; See Luther, G.W., 227; See Smith Jr, K.L., 230 Reimold, R.J., 501; 600 Reise, K. See Nacken, N., 492; 597 Reisemann, M. See Eicken, H., 163 Reistad, M. See Breivik, L.-A., 115 Ren, M., 245; 304 Renard, R. See Hoffmann, L., 415 Renaud, P.E. See Ambrose, W.G., 188, 206, 222 Rendell, A.R. See Greenwood, J.E., 164 Renk, H., 449; 486 Renouard, D. See Aelbrecht, D., 111 Renouf, L., 406; 421 Rex, M.A., 315, 317, 322, 324, 325, 326, 327; 339; See Chase, M.R., 335; See Levin, L.A., 342; See Watts, M.C., 342 Rey, A.R., 492, 552; 600 Rey, F. See Kögler, J., 97; 126; See Wassmann, P., 232 Rey, V. See Arnoux-Chiavassa, S., 112 Rey, V. See Guan, C., 122 Reynolds, J.A., 366; 421 Reynolds, L.F., 280; 304 Reynolds, M.D. See Moore, P., 32 Reynolds, P. See Martin, A.R., 595 Reynolds, R.W. See McPhaden, M.J., 31 Reynolds III, J.E., 492, 514, 515, 527, 529, 567; 601; See Morales-Vela, B., 596 Reyss, J.P. See Witbaard, R., 232 Rho, S. See Hur, S.B., 416 Rhoads, D.C., 172, 188; 229 Riaux-Gobin, C., 148, 153; 167; 173, 181, 185, 187, 192, 193, 194, 199, 200, 202, 205, 208; 229; See Pinturier-Geiss, L., 229 Ribberink, J.S. See Davies, A.G., 117 Ribes, M. See Sala, E., 421 Ribic, C.A., 514; 601 Ricci, C. See Palmer, M.A., 339

653

AUTHOR I ND E X

Rice, A.L., 172, 175, 188, 195, 199, 200, 201, 203, 211; 229; 312, 328, 329; 339; See Bett, B.J., 313, 328; 335; See Billett, D.S.M., 222, 335 Rice, S.A., 269; 304 Richardson, J.R. See Grange, K.R., 298 Richardson, M.G. See Martin, A.R., 595 Richmond, C.S. See Webb, D.J., 140 Richmond, C.E., 272, 279; 304 Richmond, N.T., 388, 389; 421; See Ebert, T.A., 414 Rick, H.-J. See Pohlmann, T., 133 Rickards, L. See Kilonsky, B., 31 Rickards, L.J., 13; 33; See Dowell, S.L., 13; 29; See Woodworth, P.L., 1–35 Ricketts, E.F., 493, 498, 533; 601 Riddle, A.M., 74; 134 Ridley, D.M. See Studier, E.H., 604 Riebesell, U., 185, 201; 229; See Alldredge, A.L., 222; See Gleitz, M., 164 Riemann, F., 177, 185, 192, 195, 206, 214; 229; See Thiel, H., 231, 341 Riera, M. See Álvarez, A., 111; See Pinot, J.-M., 133 Ries, J.C. See Chambers, D.P., 28; See Chelton, D.B., 29; See Tapley, B.D., 34 Riethmüller, R. See Lane, A., 127 Rigoni, F. See Andreoli, C., 162 Rijkeboer, M. See Otten, J.H., 228 Rijstenbil, J.W., 149; 167 Rinne, J.N., 573; 601 Rintoul, S.R., 3; 33 Rippeth, T.P., 66, 98; 134; See Burchard, H., 115; See Simpson, J.H., 98; 136; See Watts, L.J., 140 Rippingale, R.J., 272, 274, 278; 305 Ris, R.C., 100; 134; See Booij, N., 114 Riser, K.L. See Dayton, P.K., 413 Rissik, D., 285; 305 Ritz, D.A. See Nyan Taw, 275; 303 Ritzrau, W. See Graf, G., 225 Rivas, D. See Andrew N.L., 343–425 Rivero, F. See Péchon, P., 132 Rivers, J.S. See Peckol, P., 267, 268; 303 Rixen, T., 183, 187, 206; 229 Roache, P.J. See Dietrich, D.E., 119 Robblee, M.B. See Smith, T.J., 306 Robert, J.C. See Fojt, E., 585 Roberts, C.M. See Ormond, R.F.G., 333; 339 Roberts, M.J., 73; 134 Roberts, M.B. See Bates, M.R., 577

Roberts, R. See Kühl, M., 165 Roberts, S. See Zoppou, C., 55; 141 Robertson, A., 511; 601 Robertson, C.Y. See Eckman, J.E., 336 Robertson, G.J. See Gilchrist, H.G., 129; 514; 587 Robinson, A.R., 203; 229 Robinson, A.V. See Smithson, M.J., 33 Robinson, D.H. See Arrigo, K.R., 162; See DiTullio, G.R., 224 Robinson, G.J. See Mason, D.C. Robinson, J.E. See Bates, M.R., 577 Robinson, S., 551; 601; See Wasson, B., 308 Robinson, S.M.C., 373, 374, 399; 421; See Balch, T., 410 Robinson, S.M.L. See Andrew N.L., 343– 425 Robson, B.J., 280; 305 Rodda, G.H., 528, 572; 601 Rodhe, J. See Broström, G., 79; 115 Rodhe, J. See Rosenberg, R., 487 Rodriguez, C.A., 245; 305 Rodriguez, D.H., 552; 601 Rodriguéz, L. See Zegers, J., 425 Rodriguez-López, M.A. See Mignucci-Giannoni, A.A., 596 Rodríguez Sanchez Arevalo, I. See Alvarez Fanjul, E., 112 Røed, L.P., 46, 47, 64, 109; 134, 135; See Hackett, B., 64; 122 Roels, O.A. See Malone, T.C., 228 Roelvink, J.A. See Nicholson, J., 131 Roemer, S.C. See Hoagland, K.D., 164 Rogan, E. See Berrow, S.D., 527; 577 Rogers, A.D., 326; 339 Rogers, P.M., 520, 521; 601 Rogers, S.G., 284; 305 Rogers, T.L., 552; 601 Rogers-Bennett, L., 369; 421; See Karpov, K.A., 416 Rohardt, G. See Smetacek, V., 168 Röhner, M., 473; 487 Roletto, J. See Gerber, J.A., 587 Roller, R.A., 272, 273, 279; 305 Romanenkov, D.A. See Androsov, A.A., 112 Romero, M. See Cota, A., 413; See Palliero, J.S., 420 Rommel, S.A. See Reynolds III, J.E., 492, 514, 515, 527, 529, 567; 601 Rooney, A.A. See Crain, D.A., 581 Roper, D.S., 275; 305 Rosati, A. See Galperin, B., 121; See Pinardi, N., 133

654

AUTHOR I ND E X

Rose, D.L. See Konai, M., 592 Rose, L. See Jacoby, C., 590 Rose, M.D., 498, 535; 601 Rosenberg, D.M., 235; 305 Rosenberg, G. See Roy, K., 340 Rosenberg, R., 427– 489; 428, 430, 431, 434, 446, 449, 452, 453, 460, 461, 463, 464, 465, 466, 467, 468, 469, 471, 472, 473, 475, 476; 486, 487; See Baden, S.P., 478, 489; See Bonsdorff, E., 479; See Diaz, R.J., 471, 472, 473, 476, 477; 480; See Elmgren, R., 481; See Josefson, A.B., 465; 482; See Loo, L.O., 460; 484; See Nilsson, H.C., 464, 465, 466; 484; See Pearson, T.H., 430, 437, 460, 468, 472, 473, 476; 486 Rosenthal, W., 49; 135; See Schneggenburger, C., 135, 136 Rosenzweig, M.L., 323; 339 Röske, F., 54, 107; 135 Rosón, G., 66; 135 Rosowski, J.R. See Hoagland, K.D., 164 Ross, B.P., 497; 601 Ross, E.H. See Conte, M.H., 223; See Deuser, W.G., 313; 336 Ross, G. See Waddell, B.J., 424 Ross, H. See Thompson, P., 605 Ross, M.A., 382 Ross, P.S., 567; 601 Ross, T., 21; 33; See Nichols, D.S., 166 Rossello, M.A., 556; 601 Rossignol, M.L. See Sanderson, J.C., 422 Rosson, A. See Castilla, J.C., 412 Rotenberry, J.T. See Chase, M.K., 580 Roughgarden, J., 405; 421 Roullet, G., 47, 81; 135 Roussenov, V., 46, 79, 80; 135; See Pinardi, N., 133; See Simeonov, J., 136; See Stanev, E.V., 48; 137 Roussenov, V.M. See Stanev, E.V., 48; 137 Roux, J.-P. See De Villiers, D.J., 551, 552; 582 Rowden, A.A. See Attrill, M.J., 576; See Jago, C.F., 226 Rowe, G.T., 323, 327; 340; See Carney, R.S., 335 Rowles, T. See Scholin, C.A., 602 Rowley, R.J., 370; 421 Roy, G.D., 60; 135 Roy, H. See Jorgensen, B.B., 226 Roy, K., 323; 340 Roy, P.S., 242; 305 Roy, S. See Vincent, W.F., 149; 169

Rozema, J. See Bakker, J.P., 576 Rozhkov, V.A., 102; 135 Ruardij, P. See Baretta, J.W., 113 Rubin, H. See Sladkevich, M., 137 Rubython, K.E., 20; 33 Rudd, R.L. See Barnett, B.D., 492, 537; 576; See Johnston, R.F., 516; 591 Ruddick, K.G., 70, 75, 85, 97; 135; See Luyten, P.J., 128; See Tartinville, B., 138 Ruddle, K., 358, 359; 421 Rudek, J. See Mallin, M.A., 302 Rudge, S.A. See Jones, S.R., 591 Rudnick, D.T., 214; 230 Ruello, N.V., 277; 305 Ruffa, A. See Faye, B., 584 Ruiz-Villareal, M. See Gomez-Gesteira, M., 122; See Taboada, J.J., 138 Rumantsev, V. See Pitkänen, H., 486 Rumble, J. See Woodby, D., 425 Rummel, R. See Balmino, G., 28 Rumohr, H., 431, 437, 450, 476; 487; See Andersin, A.-B., 478; See Weigelt, M., 459, 468, 472, 475; 489 Rumohr J. See Fohrmann, H., 121 Rundle, S.D. See Attrill, M.J., 576 Rusek, J. See Brussaard, L., 335 Russell, C.T. See Kivelson, M.G., 165 Russell, E.S., 402; 421 Russell, M.P. See Ebert, T.A., 370; 414 Russell, N.J., 143, 144; 167 Russell, R.H. See Miller, F.L., 596 Russell, T.F. See Healy, R.W., 44; 123 Rutgers v.d. Loeff, M. See Gleitz, M., 164 Rutgersson, A. See Omstedt, A., 59, 66; 132 Rutin, J., 520; 601 Rutter, M. See Nedwell, D.B., 152; 166 Ruxton, J. See Ellwood, J., 530; 584 Ryabtsev, Yu. N., 64; 135 Ryan, C.J., 510; 601 Ryan, J.J. See Dewailly, E., 582 Ryan, J.M., 572; 601 Ryan, K.G. See McMinn, A., 166; See Trenerry, L.J., 169 Ryan, P.A., 280; 305 Rybaczak, P. See Rosenthal, W., 135 Rybaczok, P. See Lane, A., 127 Rydberg, L., 460, 476; 487 Ryder, J.L. See Blight, L.K., 578 Rygg, B., 466; 487; See Berge, J.A., 479 Rykhlikova, M.E. See Spaans, B., 603 Ryrie, S.C., 52; 135

655

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Rysgaard, S., 149, 161; 167; See Kühl, M., 165 Ryu, H.Y. See Yoo, S.K., 425 Sabanskas, M. See McPherson, B.F., 302 Sabater, F. See Palmer, M.A., 339 Sabates, A., 249, 285; 305 Saenger, P. See Bucher, D. 239; 295; See Hutchings, P., 522; 590 Sætra, Ø. See Breivik, Ø., 103, 107; 115 Sætre, R. See Aure, J., 475 Sagaut, P. See Brunet, S., 115 Saggiomo, V. See Guglielmo, L., 164 Saint Martin, G. See Faye, B., 584 Saito, K., 394; 421 Sajjadi, S.G., 75, 76; 135 Sakai, K., 269; 305 Sakshaug, E. See Smith Jr, W.O., 205; 231 Sala, E., 384, 390, 397, 399, 401; 421; See McClanahan, T.R., 399; 418 Salathe, T. See Green, A.J., 298 Saldaña J. See Ibàñez, C., 124 Salini, J.P. See Blaber, S.J.M., 294 Saliot, A. See Pinturier-Geiss, L., 229 Salomon, J.C., 73, 108; 135; See Breton, M., Saloranta, T.M., 82; 135 Salovius, S. See Vahteri, P., 488 Salter, E., 537; 601 Salter, R.E., 564; 601 Salusti, E.S. See Androsov, A.A., 112 Samelius, G., 532, 572; 601 Sammut, J., 243, 253, 284; 305 Samuel, S., 80; 135 Samuels, A.J., 565; 601 Sancetta, C., 193; 230 Sanchez, A. See Carriquiry, J.D., 245; 295 Sanchez, A.L., 568; 602 Sanchez, L.M. See Campos, E., 579 Sanchez, O. See Campos, E., 579 Sanchez, S. See Lavin, M.F., 241, 242, 245; 301 Sánchez-Arcilla, A. See Maidana, A.M., 129 Sanchez-Arevalo, I.R. See Albiach, J.C.C., 111 Sanchez-Gil, P. See Yanez-Arancibia, A., 608 Sand, M. See Witte, U., 232 Sandberg, E. See Bonsdorff, E., 479 Sandberg-Kilpi, E., 472, 474; 487 Sander, F. See Kidd, R., 275; 300 Sanders, H.L., 313, 315, 320, 321, 323, 325; 340; See Grassle, J.F., 323; 337; See Hessler, R.R., 313; 337; See Rex, M.A., 339 Sanderson, J.C., 389; 422 Sanderson, K. See Nichols, D.S., 166

Sanderson, N.T. See Brown, T.E., 295 Sand-Jensen, K. See Duarte, C.M., 555; 583 Sandler, H. See Andersin, A.-B., 439, 440, 441; 478; See Laine, A.O., 483 Sandven, S. See Johannessen, O.M., 125 Sanford, L.P., 211; 230 Sano, M., 405; 422 Sansone, E. See Napolitano, E., 131 Santelices, B. See Vásques, J.A., 424 Santos, A.P, 46; 135; See Taboada, J.J., 138 Sanyal, P., 529; 602 Sargison, N.D., 557; 602; See Clark, R.G., 580 Sarhan, T. See Lafuente, J.G., 127 Sarkisyan, A.S., 45, 86, 103; 135 Sarmento, A. See O’Connor, B.A., 131 Sarnthein, M. See Bruland, K.W., 223 Sars, G.O., 312; 340 Sasaki, R. See Sano, M., 422 Sato, M. See Yoshida, S., 608 Satoh, J. See Yano, K., 425 Saunders, G. See Meek, P.D., 529, 572; 596 Saure, A. See Peinert, R., 228 Saure, G. See Robinson, A.R., 229 Sauve, L. See Dewailly, E., 582 Savchuk, O. See Leppänen, J.-M., 484 Sayles, F.L. See Martin, W.R., 185, 205, 208; 228 Schaefer, J. See Richmond, N.T., 421 Schaefer, J.A., 562; 602 Schaefer, J.M. See Buckingham, C.A., 578 Schaefer, M.B., 365, 402; 422 Schaefer, V.H., 573; 602 Schäfer, W., 493; 602 Schaff, T., 315, 326, 327; 331, 340 Schaff, T.R., 328, 331; 340 Schaffelke, B., 267; 305 Schäffer, H.A., 53; 135; See Agnon, Y., 111 Scharek, R., 187, 206; 230; See Gleitz, M., 163; See Grossmann, S., 164; See Smetacek, V., 168 Scharf, G. See Waddell, B.J., 424 Schaumann, K. See Dick, S., 119 Scheffner, N.W. See Westerink, J.J., 140 Scheibling, R.E., 347, 379, 381, 382, 392, 399, 401, 403, 407; 422; See Balch, T., 398; 410 Scheltz, A. See Graf, G., 225 Schertzer, D. See Marguerit, C., 129 Schiavini, A.C.M. See Rey, A.R., 492, 552; 600 Schiedek, D., 276; 305; 474; 487; See Hahlbeck, E., 482

656

AUTHOR I ND E X

Schiel, D.R., 492; 602; See Foster, M.S., 400; 414 Schirmer, M. See Schuchardt, B., 305 Schlacher, T.A., 234, 235, 250, 252, 254, 283; 305; 319, 320; 340 Schlatter, A. See Simeone, R.P., 492, 494, 560; 602 Schlax, M. See Shum, C.K., 33 Schlieper, C. See Theede, H., 488 Schmidt, M., 60, 61, 89; 135 Schmidt-Nia, R. See Pohlmann, T., 133; See Puls, W., 134 Schmitt, F. See Marguerit, C., 129 Schmitz, G.H.W. See van Katwijk, M.M., 308 Schmitz, J. See Horton, C., 124 Schnack, D. See Ohldag, S., 485 Schnack-Schiel, S.B., 144, 147; 168; See Thomas, D.N., 168 Schneggenburger, C., 49, 100; 135, 136 Schneider, D.C. See Cummings, V.J., 581 Schnell, R.C. See Sturges, W.T., 168 Schnute, J., 365; 422 Schoenauen, R. See Brasseur, P., 115 Schoﬁeld, C.L. See Driscoll, C.T., 296 Schoﬁeld, N.J. See Lukatelich, R.J., 302 Scholin, C.A., 553 Schönfeld, W., 73; 136; See Dick, S., 119; See Pohlmann, T., 133 Schönfeldt, H.J., 85; 136 Schoning, K. See Westman, P., 489 Schoonees, J.S., 51, 92; 136 Schöttler, U., 472, 475; 487; See Schiedeck, D., 276; 305 Schrag, D.P. See Hoffman, P.F., 164 Schrama, E.J.O. See Lefèvre, F., 31 Schriek, R., 149; 168 Schriever, G. See Thiel, H., 231, 341 Schrimpf, W. See Eiﬂer, W., 45; 120 Schroeder, W.W. See Harris, P.T., 298 Schroeter, S.C., 371, 398, 405; 422; See Andrew N.L., 343– 425; See Cameron, R.A., 386; 412; See Dixon, J.D., 413; See Ebert, T.A., 414; See Kato, S., 365, 367, 368, 369, 407; 417 Schrum, C., 81, 82, 88; 136; See Becker, G.A., 113 Schubel, J.R. See Jassby, A.D., 229 Schuchardt, B., 253; 305 Schulz, R. See Graf, G., 225 Schuster, W. See Eicken, H., 163 Schuttelaars, H.M., 93; 136 Schwartz, M.K. See Hiruki, L.M., 589 Schwartz, M.W. See Harcourt, A.H., 495; 588

Schweitzer, S.H. See Krogh, M.G., 494; 592 Schwiderski, E.W., 23; 33 Schyberg, H. See Breivik, L.-A., 115 Sclavo, M. See Monbaliu, J., 130 Scofﬁn, T.P. See Tudhope, A.W., 175, 191, 202, 207; 231 Scott, C.A., See Crooks, K.R., 581 Scott, R. See Gray, A.J., 588 Scottish Marine Biological Association, 175, 177, 194; 230 Scottish Natural Heritage, 506; 602 Scow, K.M. See Green, C.T., 212; 225 Seaman, G.A., 534; 602 Searing, G.F. See Demarchi, W.M., 582 Sebastian, A.C. See Bennet, E.L., 577 Sebens, K.P. See Witman, J.D., 397, 401; 425 Secretin, Y. See Heniche, M., 123 Sedinger. See Anthony R.M., 575 Seed, R. See Moore, P.G., 493; 596 Seifert, T. See Schmidt, M., 135 Sein, D.V. See Tejedor, L., 138 Seire, A., 441, 468; 487 Seiser, P.E. See Anthony R.M., 575 Seisuma, Z. See Andrushaitis, A., 478 Seki, T. See Sano, M., 422 Sellmann, L. See Bornemann, H., 578 Sellschopp, J. See Onken, R., 43, 64; 132 Selvi, F. See Bigazzi, M., 494; 577 Semovski, S.V., 97; 136 Sempere, A. See Arnould, C., 575 Semroud, R. See Le Direac’h, J.P., 418 Semtner, A.J., 43, 65, 66; 136 Senjyu, T., 22; 33 Sennéchael, N. See Février, S., 121 Seppala, M., 506; 602 Seppänen, S. See Laine, A.O., 472; 483 Sepulveda, A. See Thiel, R., 307 Serafy, J. See Irlandi, E., 299 Serafy, J.E., 234, 284; 305 Serena, M. See Gardner, J.L., 572; 586 Sergeant, D.E., 514; 602 Serrano, C. See Bückle, F., 412 Servais, P. See Billen, G., 97; 114 Severinsen, T. See Kleivane, L., 592 Sevick, S.H. See Studier, E.H., 604 Seward, L.C.N., 405; 422 Sewell, M.A. See Levitan, D.R., 403; 418 Seymour, R.S. See Smith, P.A., 603 Shadbolt, C.T. See Nichols, D.S., 166 Shaﬁr, S.H. See McClanahan, T.R., 397; 418 Shaltout, K.H., 501, 503; 602

657

AUTHOR I ND E X

Sham, C.-H. See Valiela, I., 307 Shamsudin, L., 573; 602 Shankar, D., 21; 33 Shapiro, G.I., 71; 136 Shapiro, N.B. See Ryabtsev, Y.N., 52; 135 Shapiro, R., 72; 136 Sharma, A. See Sidle, R.C., 573; 602 Sharp Jr, H.F., 525; 602 Sharples, J., 47, 77, 78, 98; 136 Shaw, R.F. See Norcross, B.L., 279; 303 Shaw, T.J. See Lauerman, L.M.L., 227 Shchepetkin, A. See Marchesiello, P., 129 Shea, D. See Weisbrod, A.V., 607 Shears, N.T. See Babcock, R.C., 410 Sheaves, M., 283; 305 Sheffner, N.W. See Luettich, Jr, R.A., 128 Sheldon, F. See Puckridge, J.T., 304 Sheldon, P.R., 323; 340 Sheldrick, M.C. See Kuiken, T., 593 Shen, H. See Eicken, H., 163 Sheng, J., 43, 59, 66; 136 Shepherd, S.A. See Boudouresque, C.F., 411; See Larkum, A.W.D., 300 Sheridan, P.P., 144, 157, 161; 168 Sherwood, C.R., 82; 136 Shetye, S.R. See Shankar, D., 21; 33 Shi, F., 48, 85; 136 Shi, X.B. See Røed, L.P., 47; 134 Shigemi, Y. See Lim, C.P., 418 Shih, H.H. See Porter, D.L., 15; 33 Shilin, B.V. See Gorny, V.I., 122 Shillaker, R.O. See Gibson, J.A., 522; 587 Shimada, K. See Suzaki, A., 604 Shimanaga, M. See Kitazato, H., 226 Shipley, L.A. See Brown, W.K., 579 Shirayama, Y. See Kitazato, H., 226 Shires, R. See Gooday, A.J., 336 Shishkina, O.D. See Bertram, V., 114 Short, F.T., 255, 259, 261, 262; 305, 306 Short, J., 515, 523; 602 Shorten, R. See Thorsen, M., 605 Shpaer, I. See Leppänen, J.-M., 484 Shum, C.K., 22, 24; 33; See Chambers, D.P., 28; See Tapley, B.D., 34; See Woodworth, P.L., 35 Shurin, A.T., 446, 447; 487 Shushkina, E.A. See Oguz, T., 131 Shutz, B.E. See Tapley, B.D., 34 Sibert, J. See Hilborn, R., 402; 415 Sibuet, M., 175, 177, 190, 194, 216; 230; See Cahet, G., 213, 214; 223; See CossonSarradin, N., 335

Siddorn, J. See Allen, J.I., 111 Sidhu, H.S. See Hearn, C.J., 123 Sidle, R.C., 573; 602 Siedler, G., 1; 33 Siegel, D.A., 206; 230 Signell, R.P., 101; 136 Signoret, J.P. See Arnould, C., 575 Sikes, E.L. See Volkmann, J.K., 169 Silberstein, K., 263; 306 Silina, N. See Leppänen, J.-M., 484 Silva, A. See Martins, F., 129 Silva, J. See Bay-Schmith, E., 411 Silva, M., 505; 602 Silva, N. See Clément, A., 412 Silvagni, P. See Dailey, M.D., 582; See Scholin, C.A., 602 Silver, M. See Scholin, C.A., 602 Silver, M.W. See Alldredge, A.L., 192, 206; 222 Simberloff, D., 287, 290; 306 Simenstad, C.A., 397; 422; See Duggins, D.O., 583 Simeone, R.P., 492, 494, 560; 602 Simeonov, J., 45; 136 Simioli, A. See Pierini, S., 45, 80, 86; 133 Simmonds, D.J. See Ozanne, F., 132 Simons, M.J. See Roper, D.S., 305 Simons, T. See Holler, N.R., 589 Simpson, C.J. See Gordon, D.M., 298 Simpson, J.H., 86, 98; 136; 249; 306; See Charnock, H., 116; See Czitrom, S.P.R., 117; See Rippeth, T.P., 66; 134; See Sharples, J., 77, 98; 136; See Souza, A.J., 86; 137 Simpson, J.G. See De Long, R.L., 582 Sinclair, A.H. See Domburg, P., 582 Sinclair, M., 242, 251, 277, 278; 306 Singh, O.P., 21; 33 Singleton, D. See Sanchez, A.L., 602 Singleton, R.J. See Grange, K.R., 298 Siniff, D.B. See Watt, J., 606 Sinisalo, B. See Kraufvelin, P., 483 Sirkes, Z. See Chassignet, E.P., 116 Sirven, J. See Février, S., 121 Sissenwine, M.P. See Mace, P.M., 370; 418 Sisson, C. See Harris, L.G., 415 Sittler, B., 532; 602 Sivasothi, N. See Ng, P.K.L., 513, 519, 542; 597 Sivertsen, T. See Prestrud, P., 600 Sjare, B. See Smith, T.G., 547; 603 Sjöblom, V., 438, 446, 447; 487

658

AUTHOR I ND E X

Skaare, J.U., 568; 603; See Kleivane, L., 592 Skefﬁngton, M.J.S. See Curtis, T.F.G., 501, 503; 581 Skerratt, J. See McMinn, A., 166 Skinner, J.D., 533; 603 Skirnisson, K., 531; 603 Sklar, F.H., 253, 254; 306 Sklenar, S.A. See Heck, K.L., 298 Sklepkovych, B.O., 532; 603 Sklyar, S. See Kleine, E., 81; 126 Skogen, M. See Laane, R.W.P.M., 126 Skogen, M.D., 46, 95; 136, 137; See Smith, J.A., 137 Sköld, M. See Nilsson, H., 462; 484 Skoog, A. See Thomas, D.N., 168 Skourup, J. See Büchmann, B., 115 Skreslet, S., 34; 137; 235, 236, 276; 306 Sladkevich, M., 60; 137 Slate, J. See Pemberton, J.M., 599 Slattery, P.N. See Oliver, J.S., 339 Sleeman, D.P., 546; 603 Sleigh, M.A. See Archer, S.D., 162 Slinn, D.J. See Le Gall, A.C., 127 Sloan, C.A. See Varanasi, U., 606 Sloan, N.A., 346, 354, 390, 391, 392, 403, 405; 422 Smagorinsky, J., 72; 137 Small, J. See Pelinovsky, E., 132 Small, V. See Nellis, D.W., 533, 534; 597 Smaoui, H. See Baumert, H., 113 Smedsrud, L.H. See Eicken, H., 163; See Haas, C., 164 Smetacek, V., 147, 153, 154, 156; 168; See Falkowski, P.G., 336; See Gleitz, M., 163; See Peinert, R., 228 Smetacek, V.S., 179, 193, 201; 230 Smethurst, D., 574; 603 SMF, 454; 488 Smith, A.E., 547; 603 Smith, B.D., 370; 422; See Morgan, L.E., 419; See Vadas, R.L., 424 Smith, C.L., 48, 98; 137 Smith, C.R., 172, 173, 181, 183, 187, 188, 189, 190, 192, 193, 195, 199, 200, 201, 205, 209, 211, 212, 214, 215, 216, 217, 218, 219, 220; 230; 316, 320, 324, 325, 328, 330, 331, 332, 334; 340; See Bennett, B.A., 335; See Fornes, W.L., 224; See Hoover, D.J., 337; See Kukert, H., 316, 320, 328, 330, 331; 338; See Lambshead, P.J.D., 338; See Levin, L.A., 330, 331; 338, 342; See Miller, R.J., 228;

See Snelgrove, P.V.R., 311–342; 340; See Stephens, M.P., 231 Smith, D.E. See Woodworth, P.L., 35 Smith, D.M. See Gerber, J.A., 587 Smith, G.A. See Nichols, P.D., 166 Smith, G.R. See Gildersleeve, R.R., 587 Smith, J.A., 51, 109; 137; See Flather, R.A., 89; 121; See Mason, D.C., 129; See Coltman, D.W., 580; See Pemberton, J.M., 599; See Smith, P.A., 603 Smith, J.S., 566; 603 Smith, K.L., 325, 334; 340 Smith, M. See Wilen, J.E., 425 Smith, N. See McPhaden, M.J., 31 Smith, P.A., 522; 603 Smith, R.E.H. See Stapleford, L.S., 151, 156; 168 Smith, R.G.B. See Brock, M.A., 295 Smith, R.J. See Malik, S., 594 Smith, S., 455; 488; See Juhlin, B., 483; See Lindqvist, K., 484 Smith, S.D., 79; 137 Smith, T.G., 530, 531, 546, 547; 603; See Hammill, M.O., 546, 547, 572; 588; See Watson, J.C., 400; 424 Smith, T.J., 76; 137; 269; 306 Smith, W., 314; 340 Smith Jr, K.L., 181, 188, 190, 192, 196, 199, 200, 201, 204, 206, 207, 213, 217, 218, 219; 230, 231; See Baldwin, R.J., 222; See Beaulieu, S.E., 172, 181, 190, 193, 195, 206, 207, 211, 215, 216; 222; See Drazen, J.C., 224; See Kaufmann, R.S., 217; 226; See Lauerman, L.M.L., 227 Smith Jr, W.O., 205; 231 Smithson, M.J., 24; 33; See Hughes, C.W., 20; 30; See Spencer, R., 34 Smoak, J.M. See Lauerman, L.M.L., 227 Smock, L.A. See Gardner, L.R., 586 Smolarkiewicz, P.K., 46, 71; 137 Snelgrove, P. See Smith, C.R., 340 Snelgrove P.V.R., 311–342; 317, 319, 320, 326, 328, 330, 331, 332; 340, 341 Snell, H. See Kruuk, H., 536, 537; 592 Snow, G.C., 270; 306 Snyder, H. See Snyder, N., 498; 603 Snyder, J. See Forman, S.L., 586 Snyder, N., 498; 603 Söderström, B. See Brussaard, L., 335 Soiland, H. See Skogen, M.D., 95; 137 Solana, R. See Cota, A., 413

659

AUTHOR I ND E X

Solihoglu, B. See Oguz, T., 132 Solis-Weiss, V. See Snelgrove, P.V.R., 341 Solway Firth Review, 516, 521, 543; 603 Somers, M.J., 542; 603 Sommer, S. See Pfannkuche, O., 229 Song, Y., 64; 137 Song, Y.T., 62; 137 Sonntag, W.H. See McPherson, B.F., 302 Sonu, S.C., 344, 346, 366; 422 Soreide, N.N., 14; 33 Sørensen, K. See Berge, J.A., 479; See Magnusson, J., 467; 484 Soriguer, R.C. See Rogers, P.M., 601 Sotka, E.E. See Miller, M.W., 303 Soto, R. See Castilla, J.C., 412 Soudarin, L. See Cazenave, A., 28 Soueida, R. See Kuhnlein, H.V., 593 Soukissian, O., 47, 107; 137 Soukissian, T. See Nittis, K., 131 Sourna, A., 249; 306 Sousa, R.S. See Gava, A., 587 Southward, A.J., 390, 391; 422 Southward, E. See Southward, A.J., 390, 391; 422 Souza, A.J., 67, 71, 86; 137; See Simpson, J.H., 86; 136 Spaans, B., 531; 603 Spagnol, S. See Wolanski, E., 140 Spain, A.V. See Heinsohn, G.E., 589 Span, A. See Le Direac’h, J.P., 418 Sparre, P. See Garcia, S., 414 Spaulding, M. See Wolanski, E., 140 Spaulding, M.L., 94; 137 Speakman, J.R. See Corp, N., 581 Spencer, P.B.S. See Eldridge, M.D.B., 584 Spencer, R., 3, 4, 18, 19; 34; See Cartwright, D.E., 116; See Meredith, M.P., 31; See Woodworth, P.L., 35 Spencer, T. See Möller, I, 130 Spindler, M. See Horner, R., 164 Spindler, M. See Krembs, C., 165; See Legendre, L., 165; See Schnack-Schiel, S.B., 168; See Weissenberger, J., 169 Spliid, H. See Boon, J.P., 578 Spraker, T. See Gulland, F.M.D., 588; See Scholin, C.A., 602 Spratt, D.M. See Vale, T.G., 605 Sprintall, J. See Susanto, R.D., 34 Squires, V. See Swingle, R.S., 604 Sridhar, K.R. See Arun, A.B., 576 Stacey, P.J. See Jefferson, T.A., 591

Stafford, K.J. See Sutherland, R.D., 604 Stahl, W. See Byrd, G.V., 579 Staines, B.W. See Massei, G., 595 Stainforth, D. See Killworth, P.D., 126 Staley, J.T., 144, 159; 168 Stålnacke, P., 442; 488; See Laznik, M., 484 Stämfors, B., 453; 488 Stammer, D. See Shum, C.K., 33 Stanev, E. See Grégoire, M., 122; See Pinardi, N., 133; See Roussenov, V., 135 Stanev, E.V., 48, 63, 69, 80; 137; See Simeonov, J., 136; See Staneva, J.V., 137; See Trukhchev, D., 48, 69; 139 Staneva, J.V., 80; 137, 138; See Stanev, E.V., 137, 138 Stanisci, A. See Acosta, A., 575 Stanley, D.J., 242, 244, 252; 306 Stansby, P.K., 58; 137 Stapleford, L.S., 151, 156; 168 Staples, D.J., 272, 273, 274; 306; See Vance, D.J., 308 Stark, J.S., 280; 306 Starke, A. See Dick, S., 119 Starmans, A. See Gutt, J., 225 Staskiewicz, A. See Semovski, S.V., 136 Statham, P.J. See Tappin, A.D., 138 Steel, T., 494, 536, 557; 603 Steele, S. See Barnes, D.K.A., 411 Steffe, A.S. See Bell, J.D., 294 Steffensen, E. See Bagge, O., 479 Stegeman, J.J. See Weisbrod, A.V., 607 Stegmann, P. See Peinert, R., 228 Stehn, A., 453; 488 Steiger, T. See Jumars, P.A., 226 Steimle Jr, F.W. See Mahoney, J.B., 179, 187, 195, 203, 207, 209; 227 Stein, J.E. See Varanasi, U., 606 Steinberg, P.D., 558; 603 Steiner, N., 82; 138 Steingraeber, D.A. See Painter, E.L., 598 Steinke M. See Wolfe, G.V., 169 Steller, G.W., 556; 603 Stelling, G.S. See Bijvelds, M.D.J.P., 114; See Casulli, V., 49, 55; 116 Stempniewicz, L., 547, 572; 603 Steneck, R.S., 362, 363, 397, 398; 422, 423; See Andrew N.L., 343– 425; See Bologna, P.A.X., 398; 411; See McNaught, D.C., 398; 419; See Vadas, R.L., 397, 398, 401; 424 Stenson, G.B., 542; 603 Stepanyants, Yu. See Pelinovsky, E., 132

660

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Stephens, M. See Smith, C.R., 230 Stephens, M.P., 199; 231 Stephenson, A. See Stephenson, T.A., 492; 603 Stephenson, D.B. See Février, S., 121 Stephenson, T.A., 492; 603 Sterlini, P.E. See Moore, P., 32 Steven, A. See Koop, K., 300 Stevens, D.P. See Gille, S.T., 30 Stevens, I. See Johnson, J., 43, 46; 125 Stevenson, R.D. See Limberger, D., 594 Stewart, M.S. See Wilson, O.B., 607 Stewart, R. See McShane, P.E., 419 Stewart, W. See Telfer, S., 604 Stickle, W.B. See Roller, R.A., 272, 273, 279; 305 Stienen, C. See Peinert, R., 228 Stigebrandt, A. See Wulff, F., 489 Stigzelius, J. See Laine, A.O., 483 Stillman, R.A. See Fojt, E., 585 Stirling, I., 496, 514, 546, 547, 548, 569; 603; See Hiruki, L.M., 589 Stocker, D. See Gill, R.M.A., 587 Stocker, J.R. See Donato, A.N., 119 Stockholm Vatten, 468; 488 Stoecker, D.K., 153, 161; 168; See Montresor, M., 166 Stokes, C. See Boynton, W.R., 294 Stolzenbach, K.D., 172, 179, 188, 209; 231; See Westerink, J.J., 140 Stommel, H., 48, 86; 138 Stoner, D. See Roughgarden, J., 421 Stora, G., 276; 306 Storey, K.B. See Oeschger, R., 472; 485 Storr Hansen, E. See Boon, J.P., 578 Stotz, W., 349, 406; 423; See Zamora, S., 353; 425 Strange, I.J., 522, 523, 549, 566; 604 Stratford, K., 83, 89; 138 Strayer, D. See Palmer, M.A., 339 Stripling, S. See George, K.J., 49, 72, 84; 121 Stroemberg, J. See Svavarsson, J., 341 Strub, H., 531; 604 Strub, P.T., 204; 231 Stuart, C.T. See Levin, L.A., 342; See Rex, M.A., 339 Stuart-Smith, A.K. See James, A.R.C., 496; 590 Studier, E.H., 519; 604 Sturges, W., 22; 34; See Hong, B.G., 30 Sturges, W.T., 151; 168 Sturgess, P., 571; 604 Styan, C.A., 403; 423

Subramanian, A. See Minh, T.B., 596 Suess, E. See Jumars, P.A., 226 Sugihara, G. See Courchamp, F., 581 Suijlen, J.M. See van Dam, G.C., 139 Sullivan, C.W. See Ackley, S.F., 144, 147, 152; 162; See Arrigo, K.R., 162; See Dieckmann, G.S., 162; See Fritsen, C.H., 163; See Horner, R., 164; See Legendre, L., 165; See Lizotte, M.P., 148, 152, 156, 159; 165; See Palmisano, A.C., 159; 166; See Priscu, J.C., 154, 155; 167; See Rau, G.H., 167; See Raymond, J.A., 167; See Sturges, W.T., 168 Sullivan, L.G. See Gardner, W.D., 224 Sullivan, M.J. See Wear, D.J., 308 Sullivan, T.P., 574; 604 Summers, C.F., 492, 549; 604 Summers, N.M. See Oosthuizen, W.H., 598 Summers, R.W., 531, 532; 604; See Feare, C.J., 585 Summons, R.E. See Gibson, J.A.E., 163 Sun, C.L. See Soreide, N.N., 33 Sun, M.-Y., 199; 231 Sun, S. See Brydon, D., 115 Sun, W. See Shi, F., 136 Sunarjo. See Tsuji, Y., 605 Sundbald, K. See Stålnacke, P., 488 Sunde, J. See Breivik, L.-A., 115 Sundelin, B. See Andersson, L., 478 Sündermann, J. See Baumert, H., 113; See Berlamont, J., 114; See Langenberg, H., 127; See Pohlmann, T., 133; See Puls, W., 134 Surey-Gent, S., 510; 604 Susanto, R.D., 21; 34 Sutcliffe, W.H., 276, 277; 306 Sutherland, J.P. See Moreno, C.A., 419 Sutherland, R.D., 503; 604 Suthers, I.M. See Kingsford, M.J., 240, 246, 247, 249, 250, 285; 300; See Rissik, D., 285; 305 Sutton, M.A., 566; 604 Sutton, P. See Costanza, R., 295 Suzaki, A., 492; 604 Suzuki, I. See Takatsuki, S., 604 Suzuki, K. See Kitazato, H., 226; See Takatsuki, S., 604 Svavarsson, J., 316; 341 Svelab, 435; 488 Svendsen, E. See Aksnes, D.L., 111 Svendsen, E. See Berntsen, J., 63, 77; 114; See Laane, R.W.P.M., 126

661

AUTHOR I ND E X

Svendsen, L.M. See Kronvang, B., 300 Svensson, U., 47, 79; 138 Swan, C., 543; 604 Swann, S. See Yorio, P., 608 Swanson, S.W. See Oosthuizen, W.H., 598 Swart, P.K. See Thorrold, S.R., 307 Sweitzer, R.A., 572; 604; See Waithman, J.D., 606 Swingle, R.S., 500; 604 Syamsudin, F. See Molcard, R., 32 Syrdalen, P. See Kearney, M.S., 591 Syroechkovski, E.E. See Summers, R.W., 604 Syvertsen, E.E., 152, 153; 168; See Kristiansen, S., 165 Szaniawska, A. See Hagerman, L., 472, 474; 481; See Jahn, A., 482 Szefer, P. See Falandysz, J., 481 Szilagy, B. See Baumert, H., 113 Szmant, A.M. See Miller, M.W., 303 Taboada, J.J., 46; 138; See Gomez-Gesteira, M., 122 Taguchi, S. See Hargrave, B.T., 187; 225 Tait, D. See Wasson, B., 308 Tait, R.D. See Brown, T.E., 295 Tajima, K. See Agatsuma, Y., 410 Takahashi, A. See Watanabe, M., 606 Takahashi, T. See Rau, G.H., 167; See Tsuji, Y., 605 Takanezawa, T. See Matsumoto, K., 31 Takatsuki, S., 561; 604 Takeda, S., 508; 604 Takeo, M. See Tsuji, Y., 570; 605 Takeuchi, K. See McPhaden, M.J., 31 Taki, J., 344, 396; 423 Talagrand, O. See Hoang, S., 123 Talaue-McManus, L., 382, 383; 423 Talbot, M.M.B., 263, 264; 307; See Adams, J.B., 256, 263; 293 Talipova, T. See Grimshaw, R., 122; See Holloway, P.E., 123, 124; See Pelinovsky, E., 132 Talipova, T.G., 53, 102; 138; See Ivanov, V.A.S., 124 Tallberg, P., 173, 208; 231 Tamsalu, R., 44, 99; 138; See Ennet, P., 120 Tanabe, S. See Minh, T.B., 596; See Watanabe, M., 606 Tang, C.L. See Sinclair, M., 306 Tang, Y.S. See Sutton, M.A., 604 Tangney, D., 521; 604

Tanguy, J.M. See Nicholson, J., 131; See Péchon, P., 132 Tanguy, L. See Wyer, M.D., 134, 608 Taniguchi, I. See Kalvass, P., 370; 416 Taniguchi, I.K. See Karpov, K.A., 416 Taniguchi, K., 346, 405; 423 Taniguchi, K. See Sano, M., 422 Tankersley, R.A. See Forward Jr, R.B., 279; 297 Tannerfeldt, M. See Angerbjorn, A., 575 Tannerfeldt, M. See Elmhagen, B., 584 Tapley, B.D., 3; 34; See Chambers, B.D., 28 Tappin, A. See Prandle, D., 134; See Proctor, R., 134 Tappin, A.D., 46, 73; 138 Targett, T.E. See Rogers, S.G., 305 Tarifeño, E. See Bückle, F., 412 Tarr, J.G., 498; 604 Tartinville, B., 46, 51, 74; 138 Tassin, B. See Tusseau, M.-H., 139 Tattersall, F.H. See Smith, P.A., 603 Tausnev, N. See Nazarenko, L., 131 Tavernier, J. See Jauniaux, T., 591 Taylor, A.C. See Eldridge, M.D.B., 584 Taylor, B.F. See Kretzmann, M.B., 592 Taylor, D.I. See Schiel, D.R., 492; 602 Taylor, J.A. See Magnusson, W.E., 594 Taylor, M.K. See Ferguson, S.H., 585 Taylor, R.B. See Brown, P.J., 492; 579 Taylor, R.H., 494, 523; 604 Tegner, M.J., 347, 370, 371, 386, 394, 396, 397, 398, 401, 403, 405, 406; 423; See Dayton, P.K., 368; 413; See Karpov, K.A., 416 Tejedar, B. See Alvarez, O., 112 Tejedor, L., 62; 138; See Alvarez, O., 112; See Bruno, M., 115 Telesh, I.V., 441; 488; See Alimov, A.F., 478 Telfer, S., 526; 604 Telleria, J.L., 570; 605 Temperville, A. See Davies, A.G., 117 ten Brummelhuis, P.G.J. See Vos, R.J., 139 Tengberg, A., 208; 231 Tentori, E. See Koop, K., 300 Terborgh, J., 513; 605 Terhune, J.M., 507; 605 Terlouw, E.M.C., 537; 605 Termini, D. See Tucciarelli, T., 85; 139 Terracciano, G. See Diguardo, G., 582 Terrados, J., 255, 257, 262; 307; See Duarte, C.M., 296 Tershy, B.R. See McChesney, G.J., 494, 522, 536; 595

662

AUTHOR I ND E X

Testa, J.W. See Bowyer, R.T., 578; See Duffy, L.K., 583 Tett, P., 67, 77, 97, 98; 138; See Sharples, J., 77, 98; 136; See Smith, C.L., 48, 98; 137 Tett, P.B. See Charnock, H., 116 Thain, V.M. See Kitching, J.A., 406; 416 Thais, L. See Chapalain, G., 67; 116 Thampanya, U. See Terrados, J., 307 Theede, H., 472, 475; 488; See Graf, G., 225; See Jahn, A., 472, 473; 482 Theocharis, A. See Tsimplis, M.N., 139 Theron, A.K. See Schoonees, J.S., 51, 92; 136 Therriault, J.-C. See Sinclair, M., 306 Thessalou-Legaki, M. See Kormas, K.A., 226 Thiebaut, E. See Dauvin, J.-C., 296 Thiede, J. See Bruland, K.W., 223 Thiel, C. See Kirst, G.O., 165 Thiel, H., 172, 177, 189, 190, 199, 200, 203, 204, 211, 214, 216; 231; 328; 341; See Pfannkuche, O., 228 Thiel, R., 283; 307 Thistle, D., 313, 315, 316, 324, 329, 331; 341; See Eckman, J.E., 336; See Lambshead, P.J.D., 338 Thomas, B.W. See Taylor, R.H., 494, 523; 604 Thomas, C. See Levin, L., 227 Thomas, C.J. See Levin, L.A., 227; See Twiss, S.D., 605 Thomas, C.L. See Gooday, A.J., 224 Thomas, C.L. See Levin, L.A., 338 Thomas, D.N., 143–169; 144, 145, 148, 149, 152, 153, 154, 156, 157, 158, 159, 160; 168; See Brierley, A.S., 144, 145, 150, 158; 162; See Gleitz, M., 149, 152, 153, 156; 164; See Haas, C., 164; See Herborg, L.-M., 164; See Kennedy, H., 165; See Lara, R.J., 158; 165; See Mock, T., 166; See Schnack-Schiel, S.B., 168 Thomas, M.L. See Gill, R.M.A., 587 Thomas, M.L.H., 273, 279; 307 Thomas, R.F. See Kingsford, R.T., 234; 300 Thomas, R.M. See Attrill, M.J., 576 Thompson, A.R., 493, 538, 539, 540; 605 Thompson, J.K. See Lucas, L.V., 301 Thompson, J.R. See Lemly, A.D., 301 Thompson, K.W., 87; 138 Thompson, P., 527; 605 Thompson, P.A., 154; 168 Thompson, R.C., 524; 605 Thompson, S.R. See Vassie, J.M., 34 Thoms, M.C. See Walker, K.F., 281; 308

Thomsen, L., 207, 209, 211; 231; See Davies, A.M., 118; See Graf, G., 225 Thomson, J. See Lampitt, R.S., 227 Thomson, P.C., 523; 605 Thomson, S. See Hydes, D.J., 124 Thorndike, E.M. See Gardner, W.D., 224 Thorpe, S.A., 102; 138 Thorrold, S.R., 249, 253; 307 Thorsen, M., 523, 573; 605 Thorson, G., 319; 341 Thuet, P. See Bambang, Y., 294 Thunell, R.C., 218, 219; 231 Thurow, F. See Bagge, O., 479 Thurston, M.H. See Rice, A.L., 229 Tiedje, J.M. See Brussaard, L., 335 Tierney, C. See Shum, C.K., 33 Tierney, C.C., 24; 34 Tietjen, J. See Lambshead, P.J.D., 338 Tietjen, J.H., 316; 341 Tilbury, K.L. See Varanasi, U., 606 Tillmann, U. See Dick, S., 119 Tilman, D., 323, 324; 341 Timothy, D. See Wong, C.S., 232 Tinbergen, N., 530; 605 Tinker, M.T. See Estes, J.A., 414, 584 Tintoré, J., 49, 55; 138; See Álvarez, A., 111; See Ardhuin, F., 112; See Varela, R.A., 139 Tiselius, P., 202; 231; See Kiørboe, T., 226 Tison, J.-L. See Haas, C., 164; See Mock, T., 166 Tkalich, P., 496; 605 Todd, J.F. See Moore, W.S., 131 Tokmakian, R., 16; 34; See Challenor, P.G., 19; 28 Tokmakian, R.T. See Challenor, P.G., 28; See Gille, S.T., 30 Tolman, H.L., 100; 138 Toman, M., 293; 307 Tomas, C.R., 194, 195; 231 Tomlinson, P.B., 254; 307 Tonderski, A. See Stålnacke, P., 488 Tooley, M. See Raper, S.C.B., 33 Torruco-Gomez, D. See Axis-Arroyo, J., 576 Townsend, D. See Seward, L.C.N., 422 Toyos-González, G.M. See Mignucci-Giannoni, A.A., 596 Trainer, V. See Scholin, C.A., 602 Travis, J., 393; 423 Tréguer, P. See Nelson, D.M., 166; See RiauxGobin, C., 167 Treguier, A.M., 62; 139

663

AUTHOR I ND E X

Treguier, A.-M. See Grifﬁes, S.M., 122 Trenerry, L.J., 148, 149, 161; 169 Tribe, D.E., 557, 558, 559; 605 Tribe, E.M. See Tribe, D.E., 557, 558, 559; 605 Trilles, J.-P. See Bambang, Y., 294 Trillmich, F. See Limberger, D., 594 Tringali, M. See Travis, J., 423 Trinidad-Roa, M.J., 382; 423 Triplet, P. See Fojt, E., 585 Troitskaya, Y.I. See Ivanov, V.A.S., 124 Troncoso, R. See Bustos, E., 412 Tronon, A.A. See Gorny, V.I., 122 Truesdale, V.W. See Greenwood, J.E., 164 Truitt, B.R. See Erwin, R.M., 584 Trukhchev, D., 48, 69; 139 Trull, T. See Gibson, J.A.E., 163; See McMinn, A., 166 Trzosinska, A., 450; 488; See Falandysz, J., 481 Tsarev, V.A., 55; 139 Tselepides, A. See Duineveld, G.C.A., 224 Tsimplis, M.N., 21, 22, 23; 34; 53, 68; 139; See Plag, H.-P., 23; 32 Tsuji, Y., 570; 605 Tucciarelli, T., 85; 139 Tudhope, A.W., 175, 191, 202, 207; 231 Tugrul, S. See Oguz, T., 131 Tulkki, P., 434, 438, 448, 472, 475; 488; See Bagge, P., 479 Tully, J.G. See Konai, M., 592 Tunberg, B.G., 465; 488 Tupas, L.M. See Scharek, R., 230 Turing, A.M., 95; 139 Turley, C.M., 210, 212; 231; See Gooday, A.J., 192, 211, 214, 216; 225; 331; 336; See Lochte, K., 177, 192, 194, 195, 211, 212; 227; See Thiel, H., 231, 341 Turner, B. See Short, J., 515, 523; 602 Turner, J. See Barnes, D.K.A., 411 Turner, K. See Gren, I.-M., 481 Turner, M.G., 556; 605 Turner, R.D., 330, 331, 332; 341 Turner, R.E. See Justic, D., 299; See Rabalais, N.N., 304 Turner Jr, J.W. See Bashore, T.L., 576 Turon, X., 384; 423; See Lozano, J., 418; See Palacín, C., 420 Tusseau, M.-H., 67; 139 Tusseau-Vuillemin, M.-H., 43, 45, 46, 97; 139 Twelves, J., 540; 605 Twiss, S.D., 510; 605; See Pomeroy, P.P., 599

Twyford, K.L. See Woodall, P.F., 607 Tyler, P.A., 173; 231; See Campos-Creasey, L.S., 223; See Gage, J.D., 224; 311, 324; 336 Tyrrell, M. See Harris, L.G., 415 Udy, J.W., 261, 263; 307; See Abal, E.G., 293 Uehlinger, U. See Matthaei, C.D., 302 Ugland, K.I. See Gray, J.S., 337 Uhlman, A.H. See Smith Jr, K.L., 230 Uhrenholdt, T. See Vested, H.J., 139 Uittenbogaard, R.E. See Tartinville, B., 138 Ulmke, R. See Kirst, G.O., 165 Ulvestad K.B. See Aksnes, D.L., 111 Umar, M.J., 265, 268; 307 UMF, 432, 433, 434; 488 Umgiesser, G., 57; 139; See Zecchetto, S., 141 Unal, Y.S., 21; 34 Uncles, R.J. See Harris J.R.W., 123; See Wu, Y., 140 Underhill, L.G. See Summers, R.W., 604 Underwood, A.J., 236, 237, 292; 307; See Andrew, N.L., 390, 397, 401; 410; See Chapman, M.G., 254; 295; See Glasby, T.M., 244; 297; See Kennelly, S.J., 268; 299; See Morrisey, D.J., 303 Ungar, I.A., 502; 605 Unluata, U. See Oguz, T., 131 Unverzagt, S., 428, 471; 488 Uotila, J., 82; 139 Urﬁ, J. See Goss-Custard, J.D., 587 Uri, J.S. See Vermaat, J.E., 308 Uribe, F. See Cota, A., 413 Urios, V. See Vila, C., 606 Usher, P.J. See Rosenberg, D.M., 305 Utekhina, I. See McGrady, M.J., 595 Utkin, K.B. See Kagan, B.A., 100; 136 Uychiaoco, A.J. See Wesseling, I., 308 Vadas, R.L., 361, 362, 363, 396, 397, 398, 401; 423, 424; See Andrew N.L., 343– 425; See Elner, R.W., 401; 414; See Seward, L.C.N., 422 Vader, W., 492; 605 Vahteri, P., 437, 439; 488 Valade, J.A. See Baugh, T.M., 294 Valdebenito, H. See Mauchamp, A., 595 Vale, T.G., 492; 605 Valentine, J.F. See Heck, K.L., 298 Valentine, J.W. See Roy, K., 340 Valero Delgado, F. See Eicken, H., 163 Valiela, I., 243; 307

664

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Välipakka, P. See Alimov, A.F., 478; See Pitkänen, H., 439, 440, 441, 442, 476; 486 Valk, H., 500; 605 van Aarde, R.J., 507; 605; See Ferreira, S.M., 507; 585; See Skinner, J.D., 603 Vanaltena, I. See Steinberg, P.D., 558; 603 Van Bennekom, A.J. See Eisma, D., 249; 296 van Beusekom, J. See Puls, W., 134 van Beusekom, J.E.E. See Dick, S., 119 VanBlaricom, G.R., 400; 424; See Estes, J.A., 400; 414; 540; 583; See Gerber, L.R., 414 Vance, D.J., 277; 308; See Staples, D.J., 272, 273, 274; 306 van Dam, G.C., 74; 139 van de Bund, W.J. See Olafsson, E., 228 van den Belt, M. See Costanza, R., 295 van der Kaaij, T. See Gerritsen, H., 121 van der Loeff, M.M.R., 93; 139 Van der Meer, J. See Boon, J.P., 578 van der Wal, R., 493, 521, 562; 606; See Dormann, C.F., 583 van der Weele, J. See Duineveld, G.C.A., 224 Van der Weele, J.A. See Witbaard, R., 232 van der Zee, D., 541, 542; 606 van Diggelen, R. See Esselink, P., 584 Van Dolah, F.M. See Scholin, C.A., 602 van Dyk, P.J. See van Aarde, R.J., 605 Van Gompel, J. See Jauniaux, T., 591 Vangriesheim, A., 175, 187; 231; See Auffret, G., 222; See Cosson-Sarradin, N., 335; See Lampitt, R.S., 227 Van Haren, H. See van Raaphorst, W., 231 van Katwijk, M.M., 258; 308 van Leeuwen P.J. See Evensen, G., 46, 103; 120 van Raaphorst, W., 210, 211; 231 Van Raaphorst, W. See Berlamont, J., 114 Van Rensburg, P.J.J., 536; 606 van Rijn, L.C., 92; 139 Van Sant, S.B. See Rogers, S.G., 305 Van Scoy, P.A. See Neilan, R., 32 Van Valen, L., 323; 341 van Vierssen, W. See Vermaat, J.E., 308 Van Vuran, D. See Sweitzer, R.A., 604 van Vuren, D. See Waithman, J.D., 606 Van Vuren, D.H. See Crooks, K.R., 581 Van Wagenen, B., 366; 424 Van Wagenen, R.F., 541; 606 van Weering, T.C.E. See Thomsen, L., 207, 209, 211; 231 Van Wieren, S. See van der Wal, R., 606 Van Wijnen, H. See van der Wal, R., 606

Van Woert, M.L. See DiTullio, G.R., 224 Varanasi, U., 568; 606 Varela, R. See Thunell, R.C., 231 Varela, R.A., 98; 139 Vargas, J.M. See Lafuente, J.G., 127 Varmo, R. See Elmgren, R., 481 Värnhed, B. See Lännergren, C., 453; 483 Vasconcelos, A. See Zino, F., 608 Vasil’eva, V.V. See Bertram, V., 114 Vásques, J.A., 349, 401; 423 Vassie, I. See Dickson, B., 29 Vassie, J.M., 20; 34; See Cartwright, D.E., 116; See Meredith, M.P., 31; See Rubython, K.E., 33; See Spencer, R., 3, 18, 19; 34; See Tsimplis, M.N., 34; See Woodworth, P.L., 35 Vedernikov, V.I. See Oguz, T., 131 Vedin, H. See Smith, S., 488 Vega, R. See Moreno, C.A., 349; 419 Vega-Cendejas, M.E. See Axis-Arroyo, J., 576 Vehil, R. See Estournel, C., 120; See Johns, B., 125; See Marsaleix, P., 129 Véhil, R. See Pinazo, C., 133 Veitch, C.R. Fitzgerald, B.M., 585; See Dowding, J.E., 583 Velasquez, Z. See Bahamón, N., 113 Velegrakis, A.F. See Tsimplis, M.N., 139 Vendlinski, T.J. See Jassby, A.D., 299 Verdier, M. See Arnould, C., 575 Verdier-Bonnet, C., 43, 48, 77; 139 Verduin, J.J., 99; 139 Verlaque, M., 385, 399, 401; 424; See Boudouresque, C.F., 390, 392; 411 Vermaat, J. See Terrados, J., 307 Vermaat, J.E., 255, 256; 308; See Wesseling, I., 308 Vermeer, L.A., 392; 424 Vernberg, F.J., 254; 308 Vernet, M. See Wassmann, P., 232 Verstraete, J.-M., 15; 34 Verucci, P. See Elmhagen, B., 584 Vesey-Fitzgerald, B., 560; 606 Vested, H.J., 46, 48, 49, 54, 71; 139; See Johnson, H.K., 125 Veth, C. See Lancelot, C., 127 Vetion, G. See Riaux-Gobin, C., 167, 229 Vétion, G. See Riaux-Gobin, C. Vetter, E.W., 172; 231; 327; 341 Vetter, R.D. See Oeschger, R., 472, 475; 485 Vicari, M. See Bazely, D.R., 577 Viezzoli, D. See Malabib, V., 129 Vigan, X., 46, 58; 139

665

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Viitasalo, I. See Alimov, A.F., 478 Vila, C., 566; 606 Vilibic, I., 102; 139 Villanoy, C. See Juinio-Meñez, M.A., 395; 416; See Malay, M.C.D., 418 Villanoy, C.L. See Juinio, M.A., 416 Villareal, M.R. See Burchard, H., 115 Villareal, T. See Sancetta, C., 230 Villouta, E., 398; 424; See Moreno, C.A., 419 Vincent, P. See Le Provost, C., 31; See Shum, C.K., 33 Vincent, W.F., 144, 149; 169 Viney, M. See Cabot, D., 579 Virgilio, M. See Airoldi, L., 265, 268; 294 Virginia, R.A. See Brussaard, L., 335 Virgos, E. See Telleria, J.L., 570; 605 Vismann, B., 424, 474; 488; See Hagerman, L., 472; 481; See Sandberg-Kilpi, E., 487 Visser, A.W. See Kiørboe, T., 226 Visser, P. See Balmino, G., 28 Vitale, A.F., 519; 606 Vitousek, P.M., 492; 606 Viúdez, A. See Tintoré, J., 138 Vogt, M. See van Aarde, R.J., 605 Voigt, J. See Iken, K., 226 Volker, R. See Wasson, B., 308 Volkmann, J.K., 159; 169 Voltzinger, N.E. See Androsov, A.A., 112 Volwerk, M. See Kivelson, M.G., 165 Von-Oertzen, J.A. See Fritzsche, D., 472, 474; 481 von Storch, H. See Langenberg, H., 127 Vos, R.J., 59, 104; 140; See Gerritsen, H., 121 Vosbeek, M. See de Swart, H.E., 119 Voss, R.S., 572; 606 Vösumaa, U., 77; 139 Vuorinen, I. See Hänninen, J., 437; 482; See Vahteri, P., 488 Wachenheim, D.E., 503; 606 Waddell, B.J., 365; 424; See Perry, R.I., 364, 365, 403; 420 Wadhams, P., 145, 147; 169; See Eicken, H., 163; See Haas, C., 164; See Lange, M.A., 165 Wagner, T., 151; 169 Wahle, R.A., 361, 382, 399, 405; 424 Wahr, J., 20, 25; 34 Waithman, J.D., 494; 606; See Sweitzer, R.A., 604 Waiyaki, E., 570, 572; 606 Wake, J.A. See Heinsohn, G.E., 589

Wakeﬁeld, W.W. See Reimers, C.E., 183, 195, 199, 201, 202, 208; 229; See Smith Jr, K.L., 230 Wakeham, S.G. See Beier, J.A., 222 Walday, M. See Berge, J.A., 479 Walker, J.W. See Babcock, R.C., 410 Walker, K.F., 234, 236, 239, 241, 281; 308; See Maheshwari, B.L., 302; See Puckridge, J.T., 304 Walker, P. See Young, E.F., 141 Walker, R.J. See Kivelson, M.G., 165 Wall, G., 574; 606 Wallace, A.R., 497; 606 Wallace, J.S. See Jones, K., 591 Wallage-Drees, J.M., 520; 606 Waller, U. See Ohldag, S., 485 Walne, A. See Tett, P., 77, 97, 98; 138 Walsh, A.L., 571; 606 Walsh, I., 210; 232 Walsh, J.J. See Bruland, K.W., 223 Walsh, N.E., 562; 606 Walter, J. See Kiehl, K., 592 Walters, C., 403; 424 Walters, C.B. See Howard, B.J., 590 Walters, C.J., 290; 308; See Hilborn, R., 370, 402; 415; See Perry, R.I., 420 Walters, K. See Abele, L.G., 323; 335 Walters, R.A. See See Barragy, E.J., 58; 113 Walton, M.J. See Kuiken, T., 593 Wand, U. See See Iken, K., 226 Wang, D.-P., 49; 140; See Tintoré, J., 138 Wang, J. See Deleersnijder, E., 119; See Mooers, C.N.K., 47, 51, 66; 131 Wang, J.D. See Kourafalou, V.H., 126 Wang, Y., 245; 308 Wang, Y.M. See Harangozo, S.A., 30 Wang, Z. See Dauvin, J.-C., 296 Wansick, D.E.H. See Nolet, B.A., 597 Wanzek, M. See Kirst, G.O., 165 Ward, G.M. See Benke, A.C., 294 Ward, J.F., 517, 537; 606 Ward, S. See Koop, K., 300 Warén, A. See Rex, M.A., 327; 339 Warne, A.G. See Stanley, D.J., 242, 244, 252; 306 Warneke, R.M. See Chatto, R., 527, 528, 529; 580 Warrach, K., 67, 77, 80, 98; 140 Warren, R.J. See Ratnaswamy, M.J., 493, 535, 565; 600 Warrick, R.A., 16; 34

666

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Warwick, R.M., 319, 320; 341 Warzocha, J., 449, 450; 488; See Andersin, A.-B., 478; See Falandysz, J., 481; See Kube, J., 483 Washington, W.M. See Parkinson, C.L., 81, 86; 132 Wassenaar, T.D. See van Aarde, R.J., 605 Wassmann, P., 194; 232; 476; 489; See Passow, U., 194, 201; 228; See Riebesell, U., 229 Wasson, B., 235; 308 Wastegård, S. See Westman, P., 489 Waszocha, J. See Osowiecki, A., 449, 450, 473; 485 Watanabe, M., 567; 606; See Minh, T.B., 596 Watkins, M.M. See Tapley, B.D., 34 Watson, H., 539; 606 Watson, J. See Barnett, P.R.O., 222 Watson, J. See Billett, D.S.M., 222 Watson, J.C., 400; 424 Watson, W.H. See Jury, S.H., 299 Watson-Brown, S. See Wasson, B., 308 Watt, J., 493, 541; 606 Watt, J.P., 539, 540; 607 Watt, S.D. See Hearn, C.J., 123 Watts, L.J., 98; 140 Watts, M.C., 326; 342 Watts, S.A., 393; 424 Way, S. See Raffaelli, D.G., 600 Wear, D.J., 266; 308 Wearn, R.B., 19; 34 Weavers, B.W., 572; 607 Webb, D. See Grifﬁes, S.M., 122 Webb, D.J., 47, 71; 140; See Killworth, P.D., 126 Webb, G.J.W. See Magnusson, W.E., 594 Webb, P.I. See Miller, A.P., 525; 596 Weber, J.C. See Conte, M.H., 223 Weber, J.E. See Løyning, T.B., 90; 128 Webster, F. See McClatchie, S., 338 Weeks, W. See Creaser, E.P., 361, 399; 413 Wefer, G., 145; 169; See Bruland, K.W., 223; See Fischer, G., 163 Wehausen, J.D., 562; 607 Wei, G. See Shi, F., 136 Weide, G. See Dick, S., 119 Weigelt, M., 458, 459, 468, 472, 475; 489 Weihe, R. See Forman, S.L., 586 Weis, I.M. See Cameron, M., 567; 579 Weisbrod, A.V., 567; 607 Weissenberger, J., 145, 153, 161; 169; See Eicken, H., 163

Wells, D.E. See Boon, J.P., 578 Wells, E.A. See Otte, M.J., 598 Welsh, B.L. See Jonge, V.N. de, 483 Wendler, B.W. See Malone, T.C., 302 Wenno, B.J. See Deiongh, H.H., 582 Wenzhoefer, F. See Witte, U., 232 Werlinger, C. See Bay-Schmith, E., 411 Werner, F.E. See Lynch, D.R., 48; 128 Wesseling, I., 269; 308 West, D.M. See Clark, R.G., 580; See Sargison, N.D., 602 West, R.J., 255; 308; See Meehan, A.J., 555; 596 Westerink, J.J., 58; 140; See Luettich Jr, R.A., 128 Westhoff, V., 502; 607 Westin, L. See Nissling, A., 477; 485 Westman, P., 431; 489 Westoby, M. See Bell, J.D., 294 Weston, S.A. See Harris J.R.W., 123 Wetzel, R.L. See Moore, K.A., 259; 308; See Neckles, H.A., 303 Weyland, H. See Helmke, E., 144; 164 Wheatcroft, R.A. See Jumars, P.A., 226, 338 Wheeler, R. See Lunney, D., 594; See Priddel, D., 600 Whinam, J. See Copson, G., 521; 581 White, B.N. See Malik, S., 594 White, D.C. See Nichols, P.D., 166 White, D.J.B., 520; 607 White, G.F., 244; 308 White, G.N. See Thomas, M.L.H., 307 White, I. See Sammut, J., 305 White, J.R. See Purcell, J.E., 304 Whitehead, G.K., 560; 607 Whitﬁeld, A.K., 282, 283; 308; 570; 607; See Potter, I.C., 304; See Quinn, N.W., 304 Whitﬁeld, P.E. See Kenworthy, W.J., 299 Whitford, W.G., 507; 607 Whitlatch, R.B., 320; 342 Whitney, F.A. See Wong, C.S., 232 Whittaker, J.E. See Bates, M.R., 577 Whittemore, C.T., 566; 607 Whitworth, T., 19, 35; See Woodworth, P.L., 35 Wickström, K. See Juhlin, B., 483 Widbom, B., 214, 215; 232; See Austen, M.C., 465; 478; See Josefson, A.B., 465; 482 Widdows, J. See Harris J.R.W., 123 Wiebe, W.J. See Pomeroy, L.R., 151, 157; 166 Wiegert, R.G. See Pfeiffer, W.J., 501, 525; 599 Wiencke, C. See Kirst, G.O., 144, 148, 150; 165

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Wiens, J.A., 235; 308 Wiesler, D.P. See Gassett, J.W., 586 Wigan, M., 547, 553, 557, 560, 561; 607 Wigham, B.D. See Bett, B.J., 222 Wiig, O. See Skaare, J.U., 603; See DeRocher, A.E., 582 Wilber, D.H., 278; 308 Wilen, J.E., 404; 425; See Botsford, L.W., 411 Wiles, G.J., 562; 607 Wilkinson, C.R., 269; 308 Wilkinson, G.S., 518; 607 Wilkinson, K.N. See Bates, M.R., 577 Wilkinson, M.R. See Cummings, V.J., 581 Willebrand, J. See Meincke, J., 130 Williams, A.J., 536; 607 Williams, C.T. See Harris, L.G., 415 Williams, E.H. See Mignucci-Giannoni, A.A., 596 Williams, J.J., 58, 93; 140; 177, 190, 203, 210; 232; See O’Connor, B.A., 131; See Jago, C.F., 226 Williams, M.R. See Burbidge, A.A., 579 Williams, P.M. See Smith Jr, K.L., 230 Williams, R.E., 503; 607 Williams, R.G. See Stratford, K., 89; 138 Williams, T.M. See Estes, J.A., 414; See Estes, J.A., 584; See Gardner, L.R., 586 Williamson, D.L. See Konai, M., 592 Williamson, J. See Koop, K., 300 Williamson, K., 501, 511, 522, 548; 607 Williamson, R.G. See Tapley, B.D., 34 Willis, A.J. See Packham, J.R., 520; 598 Willis, R. See Eschler, B.M., 584 Willis, T.J. See Babcock, R.C., 410 Willock, C., 525; 607 Wilson, D.E. See Studier, E.H., 604 Wilson, D.J. See Williams, J.J., 232 Wilson, G.D.F., 314, 315, 317; 342; See Paterson, G.L.J., 339; See Poore, G.C.B., 315, 316, 317, 319, 321, 322, 324, 326; 339; See Rex, M.A., 339 Wilson, H. See Tett, P., 97; 138 Wilson, J.C. See Ramsey, D.S.L., 515; 600 Wilson, J.H. See Wilson, O.B., 607 Wilson, K.J. See Ryan, C.J., 601 Wilson, O.B., 496; 607 Wilson, P.J. See Malik, S., 594 Wilson, R.S. See Gray, J.S., 337 Wilson, W.L., 530; 607 Winchell, C.S. See Keegan, D.R., 591 Windle, S.A. See Woodworth, P.L., 35

Windolf, J. See Conley, D.J., 480 Wing, S.R., 404, 405; 425; See Botsford, L.W., 411; See Morgan, L.E., 419; See Quinn, J.F., 421; See Smith, B.D., 422 Wirth, A. See Karaca, M., 126 Wiseman, W.J. See Justic, D., 299; See Rabalais, N.N., 304 Wishner, K., 183, 202; 232 Witbaard, R., 175, 190; 232; See Duineveld, G.C.A., 224 Witcomb, R.F. See Konai, M., 592 Witman, J.D., 274; 308; 397, 401; 425 Witt, G., 188, 202, 207, 213; 232 Witte, G. See Rosenthal, W., 135 Witte, U., 211, 213; 232; See Graf, G., 225 Wittgren, H.B. See Laznik, M., 484 Woinarski, J.C.Z., 522; 607; See Palmer, C., 513; 598 Wolanski, E., 109; 140 Wolf, G.A. See Lampitt, R.S., 227 Wolf, J., 85, 97, 101; 140; See Ozer, J., 132 Wolf, P.L. See Reimold, R.J., 600 Wolf, T. See Rosenthal, W., 135 Wolfe, C.A.G. See Wolfe, S.A., 607 Wolfe, G.V., 150; 169 Wolfe, S.A., 562; 607 Wolff, H. See Kirst, G.O., 165 Wolff, T., 172; 232; 313; 342 Wolgast, D.M., 209; 232 Wollenweber, B. See Raven, J.A., 167 Wong, C.S., 187, 202, 206; 232; See Stolzenbach, K.D., 231 Wood, A.G. See González-Solís, J., 587 Wood, C. See Richmond, N.T., 421 Wood, D. See Myers, K., 597 Wood, E.J.F. See Kimball Jr, J.F., 226 Woodall, P.F., 286; 309; 537; 607 Woodby, D., 370, 376, 400; 425 Woodby, D.A., 376; 425; See Andrew N.L., 343– 425; See Ebert, T.A., 414 Woodgate, R.A., 104; 140 Woodin, S.A. See Richmond, C.E., 272, 279; 304 Woodroffe, C.D. See Knighton, A.D., 592; See Mulrennan, M.E., 570; 597 Woods, J., 105; 140 Woodsum, G.C. See Livingston, R.J., 301; See Meeter, D.A., 302 Woodward, P.R. See Colella, P., 71; 116 Woodworth, P. See Balmino, G., 28; See Kilonsky, B., 31

668

AUTHOR I ND E X

Woodworth, P.L., 1–35; 15, 19, 20, 22, 23; 35; See Andersen, O.B., 28; See Church, J.A., 29; See Harangozo, S.A., 30; See Mathers, E.L., 22; 31; See Mitchum, G.T., 32; See Murphy, C.M., 32; See Neilan, R., 32; See Raper, S.C.B., 33; See Ray, R.D., 23, 25; 33; See Shum, C.K., 33; See Spencer, R., 34; See Tsimplis, M.N., 24; 34; See Vassie, J.M., See Warrick, R.A., 34 Wooldridge, T.H. See Schlacher, T.A., 234, 235, 250, 252, 254, 283; 305; 319, 320; 340 Wooster, W.S. See Gerber, L.R., 414 Wooten, M.C. See Holler, N.R., 589; See Oli, M.K., 598 Wootton, J.T., 495; 607 Woppelmann, G. See Le Provost, C., 31 World Ocean Circulation Experiment (WOCE), 2, 15, 14, 25; 35 Worthington, D.G. See Andrew, N.L., 410; See Byrne, M., 412 Worthington, D.J. See Wiles, G.J., 607 Worthy, G.A.J., 549; 607 Wortmann, J., 234, 262, 263; 309 Wozniak, B. See Semovski, S.V., 136 Wright, D.G. See Sheng, J., 136 Wright, D.A. See Purcell, J.E., 304 Wright, G.G. See Domburg, P., 582 Wright, G.J. See Jones, G.K., 299 Wright, G.R.G. See Ogilvie, S.C., 598 Wright, L.D., 252; 309 Wright, W. See Vadas, R.L., 424 Wu, P., 89; 140; See Drakopoulos, P.G., 120; See Haines, K., 73, 83, 89; 123 Wu, Y., 59, 92; 140 Wulff, F., 475, 476; 489; See Gren, I.-M., 481; See Jonsson, P., 482; See Larsson, U., 482 Wunsch, C., 3; 35; See Shum, C.K., 33 Wurgler-Hansen, J. See Rysgaard, S., 167 Wyer, M., 566; 608 Wyer, M.D., 566; 608 Wyllie-Echeverria, S. See Short, F.T., 255; 306 Wynen, L. See Robinson, S., 601 Wynne-Edwards, V.C., 566; 608 Xiaoqi, Z. See Andrew, N.L., 343– 425 Xing, J., 63, 65, 67, 70, 71, 72, 76, 77, 83, 85, 87, 91, 103; 140, 141; See Davies, A.M., 65, 103; 118; See Johns, B., 125 Xiutao, W., 245; 309 Xue, Y., 16; 35

Yaeger, C.P. See Galdikas, B.M.F., 564; 586 Yager, P.L., 211; 232; See Smith, C.R., 340 Yagi, H. See Baumert, H., 113 Yamada, A. See Dohi, H., 582 Yamada, T. See Mellor, G.L., 75; 130 Yamanaka, K.L. See Breen, P.A., 411 Yamaoka, A. See Kitazato, H., 226 Yamazato, K. See Nakano, Y., 303 Yanez-Arancibia, A., 574; 608 Yáñez-Arancibia, A. See Day Jr, J.W., 582 Yano, K., 396; 425 Yarar, M. See Green, A.J., 298 Yaxley, R. See Norris, K., 597 Yeates, J.S. See Bolland, M.D.A., 578 Yeats, P.A. See Sinclair, M., 306 Yellowlees, D. See Koop, K., 300 Yerli, S. See MacDonald, D.W., 594 Yilmaz, A. See Napolitano, E., 131 Yingst, J. See Thistle, D., 341 Yoder, J.A., 183, 187, 205; 232; See Robinson, A.R., 229 Yoklavich, M.M., 283; 309 Yong, H.S. See Takeda, S., 604 Yonge, C.M., 516, 522, 538; 608 Yoo, S.K., 377; 425; See Hur, S.B., 416 Yorio, P., 552; 608; See Blanco, G., 578 Yoshida, S., 511; 608 Yoshinaga, H. See Nakamura, T., 355, 396; 419 Young, E.F., 47, 60; 141 Young, G.C., 250, 283; 309 Yu, C.S. See Berlamont, J., 114 Yu, J.C.S. See Ozer, J., 132 Zabala, M. See Sala, E., 384; 421 Zago, C. See Bergamasco, A., 98; 114 Zahel, W., 104; 141 Zamora, S., 353; 425 Zann, L., 235; 309 Zapata, G.J.V. See Yanez-Arancibia, A., 608 Zaquali, J. See Le Direac’h, J.P., 418 Zavatorelli, M. See Young, E.F., 95; 141 Zavodnik, D. See Le Direac’h, J.P., 418 Zecchetto, S., 57; 141 Zedler, J.B. See Fong, P., 265, 266; 297 Zegers, G.P. See Kretzmann, M.B., 592 Zegers, J., 353; 425 Zelensky, N.P. See Tapley, B.D., 34 Zettler, M.L., 431; 489 Zheng, D. See O’Connor, W.P., 32 Zhu, W. See Soreide, N.N., 33

669

AUTHOR I ND E X

Zielinski, U., 156; 169 Zielke, W. See Jankowski, J.A., 125 Ziervogel, K. See Leipe, T., 227 Zijlstra, W. See Esselink, P., 584 Zimmer, C. See Kivelson, M.G., 165 Zino, F., 494, 523; 608 Zmudzinski, L., 446; 489 Zonfrillo, B., 522; 608

Zoodsma, B.J. See Baugh, T.M., 294 Zoppou, C., 55; 141 Zorita, E., 469; 489 Zubaid, A., 525; 608 Zuleta, A., 349, 406; 425; See Bückle, F., 412; See Moreno, C.A., 349; 419 Zuur, E.A.H., 48, 53, 66; 141 Zyserman, J.A. See Davies, A.G., 117

670

SYSTEM ATI C I N D E X

SYSTEMATIC INDEX

References to sections of articles are given in italics; references to pages are given in normal type. Abra alba, 459 nitida, 467 Abyssocucumis abyssorum, 216 Acanthopagrus butcheri, 283 Actitis macularia, 545 Aechmophorus occidentalis, 541 Aedes vigilax, 492 Aegiceras corniculatum, 254, 255 Aeluropus littoralis, 505 Aeromonas, 553 Afurcagobius suppositus, 283 Agouti paca, 513 Agropyron, 521 junceiforme, 520 Agrostis capillaris, 520 Akodon olivaceus, 524 Alabaminella weddellensis, 214 Alaria esculenta, 510, 558, 559 ﬁstulosa, 400 Alca torda, 531, 544 Alle alle, 547 Alopex lagopus, 530 Alpheus, 524 Amblonyx cinereus, 542 Amblyrhynchus cristatus, 536 Ammophila, 520 arenaria, 520 Amniataba caudavittata, 283 Amperima rosea, 217 Amphiura chiajei, 473 ﬁliformis, 431, 460, 462, 466, 472, 473 Anadara broughtonii, 378 granosa, 564 Anas platyrhynchos, 507 strepera, 536 Anguilla, 539 Anguillidae, 281 Annelida, 474 Anser brachyrhynchus, 530 Anseranas semipalmata, 286 Antechinus, 515 Anthocidaris crassispina, 348, 354, 355, 357, 377, 379, 387, 392, 394, 396

Anthoxanthum odoratum, 520 Aonyx capensis, 541, 542 Apodemus sylvaticus, 506, 525 Apogon rueppellii, 283 Apogonidae, 283 Aporrhais pespelecani, 465 Aptocyclus ventricosus, 541 Arbacia lixula, 390 Archaea, 322 Arctica islandica, 458, 459, 460, 472, 475 Arctocephalus australis, 552, 572 forsteri, 510, 551 galapagoensis, 552 gazella, 514, 548, 551 philippii, 552 pusillus, 533, 534 pusillus pusillus, 551, 552 townsendii, 549 tropicalis, 551 Arenicola cristata, 272, 279 Argyrosomus hololepidotus, 284 Artemisia maritima, 502 Arthrocnemum fruticosum, 505 glaucum, 505 Artiodactyla, 491; 557 Arvicola terrestris, 526 Ascophyllum, 558 nodosum, 510 Astarte, 449, 459 borealis, 448, 449, 450, 472, 475 Aster tripolium, 501, 502 Asterias rubens, 346 Asteromphalus, 193 Atherinidae, 282, 283 Atilax paludinosus, 534 Atrina pectinata, 378 Atriplex, 521, 548 (as Halimione) portulacoides, 521 barclayana, 500 portulacoides, 505 Avicennia, 522, 564 marina, 254, 255, 513

671

SYSTEM ATI C I N D E X

Axis axis, 513 axis axis, 562 Balaena mysticetus, 528 Balanus glandula, 274 Bathysiphon, 328 ﬁliformis, 214 Beggiatoa, 453, 476 Bellis perennis, 506 Bison bison, 501 Bivalvia, 472 Blennoidea, 542 Bombus, 507 distinguendus, 506, 507 hortorum, 507 jonellus, 507 lucorum, 507 muscorum, 507 Bos taurus, 494, 495, 501, 559 Bostrychia scorpioides, 501 Branta bernicla, 492, 521 bernicla nigricans, 530 canadensis, 547 leucopsis, 530, 562 Brevoortia patronus, 285 Bromus, 521 Bruguira, 522 Bubalus bubalis, 501, 513, 559, 570 Calidris alpina, 517 Calocaris macandreae, 431 Calosciurus notatus, 513 Camelus dromedarius, 513 Campylobacter jejeuni, 566 Cancer pagurus, 538 Canis aureus, 533 latrans, 517, 534 lupus dingo, 537 lupus familiaris, 513, 536 mesomelas, 533, 534 Capitella capitata, 457, 472 Capra hircus, 500, 560 Capreolus capreolus, 508 Carcharodon carcharias, 542 Carcinus maenas, 538, 543 Caretta caretta, 532 Carnivora, 491, 492; 529 Cassiopea xamachana, 272 Cebus apella, 513 apella apella, 563

Centrostephanus coronatus, 371 rodgersii, 390 Cepphus columba, 530 grylle, 544 Cerastoderma glaucum, 451 Cerataulina, 270 Ceratium tripos, 179, 195, 203 Cercopithecus aethiops, 564, 571 Cerdocyon thous, 498 Cerorhina monocerata, 525 Cervus elephas, 495, 510, 560, 561 mariannus, 562 Cetacea, 491; 527 Chaetoceros, 175, 181, 185, 194, 270 cf neogracile, 149 socialis, 177, 185, 194 Charadrius alexandrinus, 509 dubius, 509 hiaticula, 506, 517 Chelonia mydas, 286, 532, 533, 534 Chen caerulescens caerulescens, 531 rossii, 531 Chirolophis ascanii, 538 Chironomidae, 437, 441, 453, 454, 455 Chiropotes satanas satanas, 564 Chiroptera, 491, 513; 518 Chlorodesmis fastigiata, 267 Chnoospora implexa, 267 Chondrus crispus, 559 Chorda ﬁlum, 558 Chorisochismus dentex, 541 Chrysochloris asiatica, 517 Chrysaora quiquecirrha, 280 Chrysochromulina polylepis, 466 Ciliata, 540 Cladophora, 267 vagabunda, 267 Clostridium, 549 Clupeidae, 244 Cnidoglanis macrocephalus, 283 Codium fragile, 382 tenue, 264 Conchoderma, 528 auritum, 552 virginatum, 528 Concolepas concolepas, 350 Copsychus sechellarum, 523 Corbula gibba, 458, 459, 460, 465, 475 Corethron, 185 criophilum, 185, 194 Corophium volutator, 472

672

SYSTEM ATI C I N D E X

Coscinodiscus centralis, 181 Cottidae, 542 Cottus, 539 Crassostrea rhizophorae, 563 Crocidura russula, 505 Crocodylus porosus, 501 Crustacea, 472 Cryptochiton stelleri, 540 Culicidae, 562 Cupaniopsis anacardioides, 519 Cyamus rhytina, 556 Cyanobacteria, 501 Cyclograpsus, 534 punctatus, 535, 541, 542 Cymodocea, 256 nodosa, 257, 258 rotundata, 262 serrulata, 261, 262 Cynictis penicillata, 535 Cynopterus brachyotis, 519 Cystophora cristata, 514, 515 Cystoseira, 401

Emerita brasiliensis, 279 Emiliania huxleyi, 175, 179, 181, 194 Enhalus, 256 acoroides, 262 Enhydra lutra, 513, 540 lutris, 365, 399 Ensis macha, 350 Enteromorpha, 264 intestinalis, 264, 265, 266 torta, 501 Eonycteris spelaea, 513 Epistominella exigua, 214 Equus asinus, 493 caballus, 501, 556 Eretmochelys imbricata, 533 Erignathus barbatus, 515, 546 Erinaceus europaeus, 516, 518, 544 Eschrichtius robustus, 568 Eucalyptus, 573 Euchone papillosa, 460 Eumetopias jubatus, 377 Evechinus chloroticus, 348, 383, 384, 398

Dama dama dama, 562 Dasyprocta punctata, 513 Decapoda, 524 Delphinapterus leucas, 547 Detonula confervacea, 179, 194 Diadema setosum, 382 Diastylis rahtkei, 446, 450, 460, 472 Dicentrarchus labrax, 505 Dicrostonyx groenlandicus, 527 Dictyota ciliolata, 266 Diroﬁlaria immitis, 572 Distichalis, 501 Ditylum brightwellii, 181 Dotilla myctiroides, 508 Dugong dugon, 286, 554

Favonigobius lateralis, 283 Felis catus, 495, 536 viverrima, 512 Feresa attenuata, 527 Festuca arundinacea, 559 rubra, 502, 503, 506, 520, 559 Fissurella crassa, 524 Foraminifera, 316, 317, 326, 329 Fragilariopsis, 156 curta, 156 cylindrus, 156 kerguelensis, 185, 192 Fratercula arctica, 522, 531 Fucales, 557 Fucus, 558, 561 serratus, 559 vesiculosus f. volubilis, 501 Fulmarus glacialis, 522 Fusarium, 528

Echinocardium cordatum, 458, 463, 467 Echinodermata, 472 Echinometra lucunter, 273 Echinus, 390 afﬁnis, 216 Ecklonia radiata, 265, 266, 268 Eisenia arborea, 397 bicyclis, 405 Eleocharis, 253 Elymus farctus, 506 Elytrigia juncea, 520 Embiotocidae, 542

Gadus morhua, 547 Gaidropsarus, 540 vulgaris, 539 Galerella pulverulenta, 535 Galictis cuja, 546 Gallinago gallinago, 517 Gambusia holbrooki, 281 Gari solida, 350

673

SYSTEM ATI C I N D E X

Gastropoda, 524 Gavia imer, 541 Gecarcinus lagostoma, 523 Geochelone elephantopus, 537 Gerbillurus paeba, 505 Gerbillus andersoni allanbyi, 505 Globicephala melaena, 527 melas, 508, 527 Glyptocidaris crenulatus, 387, 388 Gobiidae, 282, 283 Gobiosoma bosc, 285 Gorilla gorilla gorilla, 571 Gracileria tikvahiae, 267 Grapsus grapsus, 537 Grus rubicundus, 286

Hippocamelus bisulcus, 562 Histoplasma capsulatum, 492 Holcus lanatus, 520 Hyaena brunnea, 533 Hydroclathrus clathratus, 267 Hydrodamalis gigas, 510, 556 Hydrurga leptonyx, 515, 551, 552 Hypoderma tarandi, 492

Haematopus bachmani, 530 moquini, 535 ostralegus, 517, 530, 544, 546 Haliaeetus pelagicus, 546 Halichoerus grypus, 507, 510, 515, 548, 551, 553, 566 Halicryptus spinulosus, 433, 434, 448, 450, 458, 459, 472, 475 Halidrys siliquosa, 558 Halimione, 521 portulacoides, 502, 521 Haliotis, 540 discus hannai, 344 midae, 542 rufescens, 398, 401, 540 Halodule, 256, 262, 555 pinnifolia, 259 uninervis, 261, 262, 555 wrightii, 256, 260, 262, 554 Halophila, 256 ovalis, 259, 262, 263, 264, 554, 555 Haploblepharus, 563 Harmothoe sarsi, 431, 433, 434, 441, 446, 447, 448, 449, 450, 454, 458, 472, 474, 475 Heliocidaris erythrogramma, 389 tuberculata, 390 Hemicentrotus pulcherrimus, 354, 355, 357, 377, 379, 387, 388, 394, 396 Herpestes, 512 auropunctatus, 533, 534 Heteromastus ﬁliformis, 449, 458, 460, 465, 472 Hexagrammidae, 542 Himanthalia lorea, 558 Hinnites gigantea, 540

Idotea viridis, 443 Iguana iguana, 508 Ilyanassa obsoleta, 272 Insecta, 524 Insectivora, 491; 516 Inula crithmoides, 505 Iridaea, 350 Isoodon, 513 Jaera albifrons, 443 Jasus lalandii, 542 Juncus bufonis, 548 gerardii, 503, 504 Lagenorhynchus acutus, 527 Lagomorpha, 491; 519 Lagorchestes hirsutus, 515 Lagostrophus fasciatus, 515 Laminaria, 399, 402, 557, 559, 560 digitata, 557, 558 hyperborea, 558 longicruris, 399 ochotensis, 354 religiosa, 403 saccharina, 558 Larus, 541, 544 [Leucophaeus] scoresbii, 551 argentatus, 530, 544 audouinii, 537 canus, 543 marinus, 552 ridibundus, 543, 544 Lathyrus japonicus, 509 Lemmus lemmus, 526 Lepas, 528, 552 cf. hillii, 528 pectinata, 528 Leptatherina presbyteroides, 283 wallacei, 283 Leptocheirus pilosus, 443 Leptonychotes weddellii, 515, 549 Leptonycteris curasoae, 518

674

SYSTEM ATI C I N D E X

Lepus capensis, 544 europaeus, 494, 521 timidus hibernicus, 521 Lessonia nigrescens, 524 Ligia oceanica, 543 Limanda limanda, 462 Limonium vulgare, 502 Lithopoma tectum, 273, 279 Littorina peruviana, 524 Lobodon carcinophagus, 514, 515 Lolium perenne, 520 Loxechinus albus, 344, 347, 348, 349, 353 Loxodonta africana cyclotis, 571 Lutra canadensis, 539, 542, 545 felina, 541 lutra, 538, 539, 569 perspicillata, 541 sumatrana, 542 Lutrogale perspicillata, 542 Lytechinus anamesus, 371 variegatus, 272, 273, 279 Macaca fascicularis, 513, 564 mulatta, 563 nigra, 564 Macoma, 438 balthica, 431, 433, 434, 437, 438, 441, 443, 447, 449, 450, 451, 453, 454, 455, 472, 473, 475 calcarea, 448, 449, 458, 472 Macquaria novemaculeata, 281 Macrocystis, 373 pyrifera, 366, 397, 401, 402, 513 Macroglossus minimus, 519 Macronectes giganteus, 551 hallii, 551 Macropus agilis, 516 rufogriseus, 515 Malaleuca quinquenervia, 501 Malpaisomys insularis, 512 Marenzelleria, 438 viridis, 431, 433, 443, 451, 453, 454, 455, 472, 473, 474 Marmosa elegans, 524 Marsupialia, 491; 515 Mastocarpus (=Gigartina) stellatus, 559 Megadyptes antipodes, 520 Meles meles, 517, 537 Melinna cristata, 465 Mellanita perspicillata, 541 Melosira, 205

Membranipora membranacea, 274, 382 Mephitis mephitis, 516 Mergus serrator, 544 Meriones tristrami, 505 Mertensia maritima, 509 Metapenaeus bennettae, 277 macleayi, 277 Microtus agrestis, 526 californicus, 526 longicaudatus, 495 oeconomus, 496 Mirounga angustirostris, 515, 552 leonina, 510, 514, 515, 549, 550, 552 Mollusca, 474 Monachus monachus, 511, 515, 549 schauinslandi, 495, 515, 549, 552, 553 Monodon monoceros, 547 Monoporeia, 438 (=Pontoporeia) afﬁnis, 433 afﬁnis, 431, 433, 434, 435, 437, 438, 441, 442, 443, 448, 453, 454, 472, 473, 474 Monostroma obscurum, 267 Mugilidae, 281 Mus domesticus, 522 musculus, 505, 511, 512, 524, 525, 536 Mustela erminea hibernica, 546 furo, 545, 546 putorius, 545 vison, 542 Mustelidae, 543 Mya arenaria, 451, 472 arenaria oonogai, 377, 378 Myriochele, 460 Mysis relicta, 431 Mytilus californianus, 540 edulis, 433, 451, 455, 463, 465 edulis galloprovincialis, 274 Nannopterum harrisi, 537 Nasalis larvatus, 564 Necora puber, 538 Neomys fodiens, 516 Neophoca cinerea, 553 Nephrops norvegicus, 461, 462, 476 Nephtys ciliata, 458 hombergii, 458 Nereis, 438 (=Hediste) diversicolor, 431 (=Neanthes) virens, 474 diversicolor, 434, 451, 457, 472, 474 virens, 474

675

SYSTEM ATI C I N D E X

Nereocystis luetkeana, 366 Nesoryzomys fernandinae, 524 swarthi, 524 Nia epidermoidea, 556 Nitzschia, 175 Noctilio leporinus, 519 Nodularia, 271 spumigena, 271 Notiomystis cincta, 523 Nyctophilus bifax, 518 Nymphaea, 253 Oceanodroma leucorrhoa, 520 macrodactyla, 536 Octopus granularis, 541 Ocypode, 563 ceratophthalmus, 563 cordimanus, 563 khüli, 563 Odobenus rosmarus, 547 rosmarus divergens, 547 rosmarus rosmarus, 514, 547 Odocoileus hemionus sitkensis, 560 virginianus, 567 virginianus clavium, 500, 513 Oligochaeta, 434, 441, 453 Omnatophoca rossi, 515 Oncorhynchus nerka, 547 Ondatra zibethicus, 505 Oneirophanta mutabilis, 216 Onychophora, 318 Ophiocten hastatum, 217 Ophiodromus ﬂexuosus, 465 Ophiura albida, 458, 460 Orchestia gammarellus, 505 Orcinus orca, 400, 528 Oreamnos americanus, 560 Oryctolagus cuniculus, 519, 544 Oryzomys, 525 longicaudatus, 524 palustris, 525 Otaria byronia (= O. ﬂavescens), 552 ﬂavescens, 551 Ovibos moschatus, 532, 560, 566 Ovis aries, 495, 501, 510, 544, 557 Padina tenuis, 267 Pagophila eburnea, 531 Palmaria (= Rhodymenia) palmata, 510, 559 palmata, 558, 559 Pandalus borealis, 466

Panicum repens, 559 Pannychia moseleyi, 216, 217 Panopea abrupta, 400 Panthera leo, 572 pardus, 572 tigris, 513, 529 Papillogobius punctatus, 283 Papio cynocephalus, 563 ursinus, 563 Paracentrotus lividus, 348, 384, 390, 391, 392, 406 Paralia (Melosira) sulcata, 194 sulcata, 177 Parameles, 513 gunnii, 573 Paramoeba invadens, 362, 381 Parastichopus californicus, 387 Parodiochloa ﬂabellata, 522 Patella, 538 Pelecanus conspicillatus, 286 Penaeus duorarum, 277 indicus, 277 merguiensis, 272, 273, 274, 277 plebejus, 277 Percursaria, 501 Perdix perdix, 544 Perissodactyla, 491; 556 Perkinsus marinus, 279 Peromyscus keeni, 525 maniculatus, 574 polionotus trissyllepsis, 570 Petrogale lateralis, 495 Phaeocystis, 150, 177, 185, 192, 194, 203, 205 Phaeophyta, 557 Phalacrocorax, 541 Phasianus colchicus, 544 Philesturnus carunculatus, 523 Phoca fasciata, 515 groenlandica, 514, 515, 546 hispida, 514, 515, 531, 546, 548 largha, 515 vitulina, 507, 515, 551 Phocarctos hookeri, 551 Phoceona sinus, 245 Phocidae, 515 Pholis gunnellus, 538, 540 Phoradendron, 513 Phoronis muelleri, 460, 465 Phragmites australis, 505 Phyllorhiza punctata, 272, 274 Physeter macrocephalus, 527, 528

676

SYSTEM ATI C I N D E X

Pinus pinea, 560 Piscicola geometra, 443 Pisidiidae, 441 Pizonyx, 519 vivesi, 519 Plagusia chabrus, 541, 542 Plantago maritima, 500, 502 Plautus alle, 531 Pleuronectiformes, 542 Plotosidae, 283 Poa, 520 trivialis, 520 Polarella glacialis, 144 Pollicipes polymerus, 274 Polychaeta, 472, 524 Polydora ciliata, 465 Pontoporeia femorata, 431, 433, 441, 442, 446, 447, 454, 472, 473, 474 Posidonia, 255, 256 australis, 555 sinuosa, 262 Potamochoerus porcus, 571 Potamopyrgus jenkinsii, 453 Praxillella, 215 Presbytis, 564 Priapulida, 472, 474 Primates, 491; 563 Procyon cancrivorus, 513, 535 lotor, 495, 535 Prototroctes maraena, 281 Prymnesiophyceae, 194 Psammechinus miliaris, 390 Pserrdocheirus peregrinus, 573 Pseudocardium sybillae, 344 Pseudocentrotus depressus, 354, 355, 357, 377, 379, 394, 396 Pseudochitinopoma occidentalis, 274 Pseudogobius olorum, 283 Pseudomonas, 553 Pseudomys novaehollandiae, 522 Pseudonitzschia australis, 553 seriata, 151, 152 Psychropotes longicauda, 216 Pterodroma macoptera gouldi, 523 Pteropus, 513 Ptychoramphus aleuticus, 536 Puccinellia maritima, 502, 503 Pufﬁnus griseus, 536, 546 opisthomelas, 536 pufﬁnus, 561 Pycnopodia helianthoides, 395

Pygoscelis antarctica, 551 papua, 549 Pygospio elegans, 447, 472 Pyrrhocorax pyrrhocorax, 511 Rangifer tarandus, 506, 512, 547, 562, 563 tarandus pearyi, 497 tarandus platyrhynchus, 562 Raphus cucullatus, 564 Rattus, 523, 524, 536 exulans, 523, 524 norvegicus, 522, 523, 524, 544 rattus, 505, 513, 523, 524 tiomanicus, 513 Reithrodontomys raviventris, 525 Rhizoclonium, 501 Rhizophora, 522, 564 Rhizosolenia, 181, 183, 185, 194, 205 Rhodophyta, 524 Rhopilema esculentum, 273, 278 Rodentia, 491, 512; 522 Rubus, 539 Ruppia, 256 cirrhosa, 258 megacarpa, 255 Rynchops niger, 495 Saduria entomon, 431, 433, 434, 441, 447, 448, 453, 472, 474 Sagina maritima, 548 Salicornia, 500 bigelovii, 500 europaea, 502, 503 fruticosa, 505 perennis, 503 Sarcophilus harrisii, 515 Sargassum, 266, 268, 328, 332, 382 baccularia, 267 ﬁlipendula, 266 microphyllum, 265 Scapanus orarius, 573 Sciaenidae, 245 Scirpus maritimus, 505 Scolopax rusticola, 544 Scoloplos armiger, 446, 447, 449, 450, 457, 458, 460, 472, 474, 475 Scorpaenidae, 542 Semibalanus cariosus, 274 Senecio jacobaea, 503, 504 Sesarma, 525 Sirenia, 491; 553

677

SYSTEM ATI C I N D E X

Skeletonema costatum, 193, 270 Somateria mollissima, 530, 536, 544 spectabilis, 532 Sonneratia, 513, 564 alba, 519 Sorex minutus, 516 ornatus sinuosus, 516 Sousa chinensis, 529 Sparidae, 283 Spartina, 501, 502, 522, 525, 535, 545, 556 alterniﬂora, 522, 556 Spergularia, 548 Sphaerechinus granularis, 390 Spheniscus humboldti, 560 magellanicus, 560 Spinifex sericeus, 516 Spiroplasma, 492 Stellaria media, 548 Stenella coeruleoalba, 528 Stercorarius parasiticus, 559 Sterna, 495, 544, 559 albifrons, 536 dougallii, 522 hirundinacea, 546 hirundo, 543, 545 macrura, 531 vittata, 498 Strongylocentrotus, 344, 347, 349, 357, 403, 541 intermedius, 344, 347, 354, 355, 357, 358, 359, 360, 374, 377, 379, 394, 396 droebachiensis, 344, 347, 348, 349, 360, 363, 364, 373, 374, 375, 376, 379, 385 392, 405 franciscanus, 344, 347, 348, 349, 363, 364, 365, 371, 372, 375, 376, 385, 388, 395, 396, 397, 398, 401, 403, 404, 405, 406, 540 nudus, 354, 355, 357, 358, 359, 360, 374, 387, 388, 394, 396, 405 pallidus, 374 polyacanthus, 374, 375 purpuratus, 348, 363, 365, 371, 372, 388, 389, 398, 401 Suaeda esteroa, 500 maritima, 500, 502 Suessiaceae, 144 Sula nebouxi, 537 Sus barbatus, 560 scrofa, 513, 560 Sylvilagus palustrishefneri, 571 Syncerus caffer, 571 Syngnathidae, 282

Synthliboramphus antiquus, 523 hypoleucus, 536 Syringodium, 255, 256, 262 ﬁliforme, 256, 262, 554 isoetifolium, 261, 262 Tabanidae, 492 Tadorna tadorna, 520 Tatera afra, 571 Taurulus bubalis, 539, 540 Telatodytes palustris griseus, 525 Teraponidae, 283 Terebellides stroemi, 431, 449, 458, 460 Thalassia, 256, 262 hemprichii, 262 testudinum, 256, 259, 260, 262, 264 Thalassionema, 175 Thalassionema/Thalassiothrix, 183 Thalassiosira hyalina, 179, 194 Thalassiothrix longissima, 219 Thalassodendron, 256 Thyasira, 467 equalis, 465 sarsi, 465 Thylogale billardieri, 515 Thymus polytrichus [as praecox], 521 Tomistoma schlegeli, 564 Totoaba macdonaldi, 245, 284 Toxocara canis, 537 Trachypithecus auratus, 564 cristatus, 564, 565 Tresus keenae, 378 Trichechus manatus, 554 manatus latirostris, 554 senegalensis, 554 Trifolium pratense, 506 subterraneum, 504 Triglochin maritimum, 500 Tringa totanus, 502, 517, 571 Tripleurospermum maritimum, 548 Tripneustes esculentus, 392 gratilla, 348, 354, 357, 382, 383, 394, 395 Trochochaeta multisetosa, 431, 449 Tropidurus albemarlensis, 537 Turbinaria ornata, 267 Tursiops aduncus, 529 truncatus, 529 Tyto alba, 512 Uca, 525 tangeri, 564

678

SYSTEM ATI C I N D E X

Ulothrix, 501 Ulva expansa, 264, 266 Umbellicosphaera sibogae, 194 Unionidae, 441 Uria aalgae, 531, 544, 549 lomvia, 526, 531, 547, 548 Urocyon littoralis, 572 Ursus americanus, 496, 547 arctos, 546, 547 maritimus, 514, 546, 547 Vallisneria gigantea, 255 Vanellus vanellus, 517, 544 Vibrio, 553 Vulpes vulpes, 495, 516, 529, 530, 533

Wallabia bicolor, 516 Xenobalanus globicipitis, 528 Xenorhynchus asiaticus, 286 Xeromys, 512 myoides, 522 Zalophus californianus, 549, 553, 568 Zoarces viviparus, 540 Zostera, 255, 256, 263, 267 capensis, 256, 258, 262, 263 capricorni, 255, 257, 258, 261, 554, 555 marina, 258, 259, 261, 262

679

SUBJ ECT I ND E X

SUBJECT INDEX

References to whole articles are given in bold type; references to sections of articles are given in italics; references to pages are given in normal type. Altimeter calibration using WOCE tide gauges, 16–18 Altimetry, 1, 2, 4, 9, 13, 15, 17, 19, 20, 21, 22, 23, 25, 26, 27, 28 ALVIN, 179, 181, 183, 189, 191 Antarctic, biogeochemistry of sea ice, 143–169 Antarctic Circumpolar Current (ACC) choke points, 2, 3, 18–20 Biogeochemistry of Antarctic sea ice, 143–169 dissolved gases, 149–151 dissolved organic matter, 156–158 effects on ice algae, 158–160 future perspectives, 160–161 inorganic nutrients, 151–156 sea-ice characteristics, 145–148 Bottom pressure recorders, 1, 3, 18–20, 23, 24 Climate change and coastal mammals, 569–570 Coastal and shelf-sea modelling in the European context, 37–141 best available schemes, 109 boundaries, 79–88 coastal boundary condition, 84–87 river inputs, 85–86 wetting, drying, 84–85 open boundaries, 87–88 sea bed boundary condition, 83–84 surface, 79–83 freshwater, salinity ﬂuxes, 81 other surface inputs, 83 sea ice, 81–83 wind stress, pressure gradients, thermohaline ﬂuxes, 79–81 currently unaddressed problems, 109–110 deﬁnitions, 41–42 future challenges, 110–111 general models, 51–52 glossary, 42–49 hydrodynamics, 52–78 advection of momentum and tracers, 70–75

680

advection-diffusion models, 73–75 horizontal diffusion, 72–73 momentum, 70–71 tracers, 72 describing, dividing the domain, 55–62 co-ordinate systems and discretisation, 55–56 curvilinear, 60–62 ﬁnite difference, 59–60 ﬁnite element methods, 56–68 ﬁnite volume, 58–59 horizontal discretisation, 56 nesting, 60 orthogonal grid transformations, 60 limited dimensions, 66–68 1-D, 67 2-D (horizontal and 3-D), 68 2-D (slice), 67–68 box, 66–67 starting point, equations, 52–54 Boussinesq equations, 53 hydraulic theory, 52–53 Korteweg – de Vries equations, 53 Navier-Stokes, Saint-Venant and primitive equations, 53–54 other methods, 54 simpler formulations, 52 the approximations, 54–58 Boussinesq, 54 hydrostatic, 54–55 time discretisation, 68–70 initial ﬁelds, 69–70 steady state or time evolving, 69 time increment, CFL criterion, 68–69 time stepping, 70 vertical co-ordinates, 62–66 double sigma co-ordinates, 63 free surface, rigid lid, 65–66 functional form, 64–65 isopycnal co-ordinates, 64 S-co-ordinates, 64 sigma co-ordinates, 63

SUBJ ECT I ND E X

vertical discretisation, 65 z-co-ordinates, 62 vertical sub-grid scale parameterisation, 75–78 behaviour of turbulence, 78 intercomparison of turbulence schemes, 76–77 simpliﬁcation, 77–78 included processes, 88–107 biology, 94–99 data assimilation, 103–105 internal waves, 102–103 operational oceanography, 105–107 sediments, 91–94 temperature, salinity, 88–91 wind waves, 99–102 sea roughness, 101 wave models, 99–101 wave/current interaction, 101–102 necessity of multiple models, 107–108 need for modelling, 38 review of reviews, 49–51 trend towards rationalisation, 108–109 Conservation and coastal mammals, 570–572 Coral reefs, effects of freshwater on, 268–269 Deep ocean tide model developments, WOCE, 23–25 Deep-sea diversity, 311–342 conclusions, 333–334 deep-sea benthos, 311–312 deep-sea diversity and why it matters, 334 diversity of deep-sea communities, 322–323 broad-scale pattern, 324–327 data needed to understand regulation of diversity, 332–333 ﬁne-scale pattern and experimentation, 327–332 overview on theories and testability, 322 theories, 323–324 diversity of deep-sea systems, 314–322 comparison of diversity and richness with other systems, 318–321 data required to estimate diversity and pattern, 322 estimates of species richness, 315–318 meaning of diversity, 314–315 habitats and communities, changing perceptions, 312–314 historical perspective, 312 modern perspective, 313–314

Diatoms, in phytodetritus 193–194 Diatoms, sea-ice, 144, 148, 149, 150, 155, 156, 158, 159, 160 Dimethylsulphide, 150, 151 Dissolved organic matter, in sea ice 156–158 Ecological effects of ﬁshing for sea urchins, 397–402 El Niño, 15, 21, 22 Enhancement of sea urchin ﬁsheries, 393–397 Eutrophication and oxygen deﬁciency, effects on Scandinavian and Baltic benthos, 427– 489 consequences for the ecosystem, 475–477 effects of hypoxia/anoxia on macrobenthic infauna, 432–467 Åland archipelago, 435–437 Baltic proper, 444–451 eastern Gotland Basin and northern Baltic proper, 446–447 open water areas, 444–446 southern Baltic proper, 448–451 western Gotland Basin, 447–448 Belt Seas and Öresund, 455–459 Kiel Bay, 458–459 Gulf of Bothnia, 432–435 Gulf of Finland, 439–442 Gulf of Riga, 442–444 Kattegat, 460–463 Danish Kattegat estuaries, 462–463 open Kattegat, 463 southeast Kattegat, 460–462 southwest Kattegat, 462 Skagerrak, 463–467 Norwegian coast, 466–467 open Skagerrak, 467 Swedish coast, 464–466 SW Finnish archipelago, 437–438 Swedish East coast, 452–455 Oland and Gotland islands 455 Stockholm archipelago, 452–455 eutrophication, 428–431 future perspectives, 477 losses of benthic macrofaunal biomass, 471–472 macrobenthic infauna, 431–432 strategies to cope with hypoxia/anoxia and hydrogen sulphide, 473–475 temporal and spatial development of hypoxia/ anoxia, 467–471

681

SUBJ ECT I ND E X

tolerance to oxygen depletion by benthic species, 472–473 Freshwater impacts on estuarine and coastal habitats, 233–309 effects of altered ﬂows on physical factors, 246–254 estuarine and offshore environments, 246–254 contaminants, 253–254 dissolved oxygen, 252–253 geomorphology, 250 nutrients, 252 salinity, 251–252 sediments, 252 temperature, 250–251 effects on habitat-forming species, 254–269 coral reefs, 268–269 macroalgae, 264–268 mangroves, 254–255 salt marsh, 254 seagrasses, 255–264 effects on other ﬂora and fauna, 270–287 birds, 286 ﬁshes, 280–286 species in discharge plumes and fronts, 285–286 species mostly in estuaries, 282–285 species mostly in freshwater, 280–281 invertebrates, 271–280 macrofauna and other invertebrates, 275–280 meiofauna, 275 zooplankton, 275 other vertebrates, 286–287 phytoplankton, 270–271 framework for the review, 236–237 implications for managers, 287–293 nature of freshwater ﬂows, 237–246 anthropogenic factors inﬂuencing freshwater ﬂow, 240–243 contaminants, 243 water ﬂow, 240–243 case studies, 244–246 Colorado river, 245 Ganges River, 246 Nile River, 244 Yellow River, 245 natural factors inﬂuencing freshwater input, 238–239 nature of impacts, 243–244

Freshwater inputs, in coastal and shelf sea models, 85–86 Geodetic developments during WOCE, 25–26 GLOSS programme, 4, 5, 14–15, 18, 19, 26, 27 Hypoxia/anoxia effects on macrobenthic infauna, 432–467 Ice-edge habitats, coastal mammals, 514–515 Lipids, in sea-ice diatoms, 159 Macroalgae, effects of freshwater on, 264–268 Mammals in intertidal and maritime ecosystems, 491– 608 ability to exploit intertidal zone and colonise islands, 496–497 Artiodactyla, 557–562 caribou/reindeer, 562 cattle, 559–560 deer, 560–562 goats, 560 pigs, 560 sheep, 557–559 Carnivora, 529–556 badgers, 537–538 bears, 546–547 cats, 536 coyotes, 535 dogs, 536–537 foxes, 529–531 hyaenas, 532 jackals, 532 little grison, 546 mink, 542–545 mongooses, 532–535 otters, 538–542 polecats, 545–546 raccoons, 535 seals and sea lions, 548–553 stoats, 546 tigers, 529 walruses, 547–548 Cetacea, 527–529 Chiroptera, 518–519 climate change and coastal mammals, 569–570 coastal mammals and salt, 500 conservation and coastal mammals, 570–572

682

SUBJ ECT I ND E X

ephemeral impacts of coastal mammals, 566–567 future research, 572–574 Insectivora, 516–517 hedgehogs, 516–517 moles, 517 shrews, 516 Lagomorpha, 519–521 hares, 521 rabbits, 519–521 mammal carcases on shorelines, 498–499 mammals and islands, 493–496 maritime and intertidal ecosystems, 500–515 caves, cliffs and lava tubes, 510–512 ice-edge habitats, 514–515 intertidal sand and mud ﬂats, 507–508 mangroves, 512–513 rocky shores, 510 saltmarshes and shore meadows, 500–505 sand dunes and machair, 505–507 seagrass and kelp beds, 513–514 shingle beaches, 508–509 Marsupialia, 515–516 nutrient cycles inﬂuenced by coastal mammals, 565–566 Perissodactyla, 556–557 pollution and coastal mammals, 567–569 predation and scavenging by coastal mammals, 497–498 Primates, 563–564 Rodentia, 522–527 lemmings, 526–527 mice, 525–526 porcupines, 527 rats, 522–525 voles, 526 Sirenia, 553–556 Management of sea urchin ﬁsheries, 402–407 Mangroves effects of freshwater on, 254–255 mammals in, 512–513 Marine snow, 171, 192, 195, 201, 202, 203, 206, 208, 211 Modelling, coastal and shelf seas, 37–141 Nutrient cycles inﬂuenced by coastal mammals, 565–566 Operational oceanography, 105–107

Phytodetritus, accumulation and fate on the sea ﬂoor, 171–232 accumulation and persistence, 206–208 composition, 192–201 chemical, 195–201 inorganic contents, 201 organic carbon, 199–201 phytopigments, 195–199 microscopic, 192–195 coccolithophorids, 194 diatoms, 193–194 other phytoplankton, 194–195 fate, 210–219 consumption by benthic fauna, 213–217 macrofauna, 215 megafauna, 215–217 meiofauna, 214–215 decomposition by bacteria, 211–213 incorporation into sediment, 218–219 resuspension, 210–211 further study recommendations, 220–221 impact on sediment-water column ﬂuxes, 208–209 particulates, 209 solutes and gases, 208–209 mechanisms leading to mass sinking, 201–206 fronts, 204–205 ice edge effects, 205–206 mesoscale eddies, 203–204 mixed layer effects, 202–203 sampling methods, 188–192 cores, 190–192 photographs, 188–190 study sites, 172–188 Pollution and coastal mammals, 567–569 Red tides, 173, 195 Salt marshes effects of freshwater on, 254 mammals in, 500–505 Sea ice, 81–83 Sea ice, biogeochemistry, 143–169 Sea level research from tide gauges during WOCE, 1–35 altimeter calibration using tide gauges, 16–18 bottom pressures and ACC choke points, 18–20 deep ocean tide model developments, 23–25 future role of global tide gauge network, 26–27

683

SUBJ ECT I ND E X

geodetic developments, 25–26 other uses of tide gauges, 21–23 sea-level measurements for WOCE, 4–15 altimeter and tide gauge plans, 4–5 GLOSS programme, 14–15 WOCE tide gauge datasets and products, 13–14 WOCE tide gauge network, 5–13 delayed mode sea level DAC, 6, 13 fast mode sea level DAC, 6, 9–13 validations of sea-level data and model simulations, 15–16 WOCE programme and sea-level requirements, 2–3 Sea urchin ﬁsheries, status and management, 343– 425 ecological effects of ﬁshing, 397–402 effects of harvesting, 397–399 ﬁsheries, 399–402 enhancement, 393–397 habitat enhancement, 396 reseeding, 393–396 transplantation, 396–397 management, 402–407 Allee effects, 403–404 ecosystem management, 407–408 metapopulations and scales of management, 404–405 minimum and maximum legal sizes, 406–407 recruitment variability, 405–406 single species assessment, 402–403 world production, 346–393 Alaska, 375–377 Australia, 389–390 Baja California, 371–373 ﬁshery, 371–372 management, 372–373 British Columbia, 363–365 assessment and status, 365 California, 365–371 ﬁshery, 365–366 ﬁshing effort and catch rates, 368 management, 368–371 production, 366–368

Chile, 349–353 ﬁshery, 349–351 management, 352–353 production, 351–352 China, 387–388 France, 390 Iceland, 392 Ireland, 391–392 Japan, 353–360 ﬁshery, 353–355 management, 358–360 production, 356–358 Maine, 360–363 management, 362–363 New Brunswick, 373–374 New Zealand, 383–384 Nova Scotia, 379–382 Oregon, 388–389 other ﬁsheries, 392–393 Philippines, 383 Russia and former USSR, 374–375 South Korea, 377–379 Spain, 384–385 Washington, 385–387 assessment and status, 386–387 ﬁshery, 385–386 management, 386 Sea-ﬂoor observatory, 220, 221 Seagrasses, effects of freshwater on, 255–264 Sediments, in coastal and shelf sea models, 91–94 Stable carbon isotopes, in sea ice, 160 Tide gauge networks, 1, 4, 5–13 Tide gauges, in WOCE, 1–35 Tracer experiments, 199, 212, 213, 214, 215, 218, 219 Tracers, in coastal and shelf sea models, 72–75 Waves, 99–102, 102–103 World Ocean Circulation Experiment (WOCE), sea-level research from tide gauges, 1–35

684

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